Detecting Well-being in Digital Communities: An Interdisciplinary Engineering Approach for its Indicators

In this thesis, the challenges of defining, refining, and applying well-being as a progressive management indicator are addressed. This work\u27s implications and contributions are highly relevant for service research as it advances the integration of consumer well-being and the service value chain. It also provides a substantial contribution to policy and strategic management by integrating constituents\u27 values and experiences with recommendations for progressive community management

Detecting Well-being in Digital Communities: An Interdisciplinary Engineering Approach for its Indicators

In this thesis, the challenges of defining, refining, and applying well-being as a progressive management indicator are addressed. This work\u27s implications and contributions are highly relevant for service research as it advances the integration of consumer well-being and the service value chain. It also provides a substantial contribution to policy and strategic management by integrating constituents\u27 values and experiences with recommendations for progressive community management

FACTORS FOR ASSESSING PROSPECTIVE DOCTORAL APPLICANT READINESS

Doctoral program admissions management is universal yet rarely addressed in Information Systems literature. Readiness, a concept well-established in educational theory, is a compelling theory upon which to further professionalize the process of reviewing application portfolios. We propose a literature-based, extensible assessment rubric for reviewing doctoral program applicant materials based on the concept of research readiness. The rubrics pay particular attention to universal competencies required for progressing from students to future IS research professionals. Unifying assessment standards for doctoral admissions facilitates faculty decision-making, while creating clear standards for prospective candidates on expectations for minimum requirements

A Research Agenda to Understand Drivers of Digital Gullibility

Gullibility is a behavior set that includes insensitivity to cues signaling untrustworthiness, the propensity to accept false information, reject true information, or taking costly risks. It is a useful lens from which to view real-world adverse outcomes driven by the online behaviors of seemingly well-intentioned, or non-malicious, individuals. Though well established in pre-internet literature, gullibility has been largely sidestepped as a driver of adverse events in the digital era despite ample evidence for its existence. To better understand the drivers and contextual factors behind digital gullibility, we propose a comprehensive research agenda which aligns open research gaps with a set of research driven propositions. The agenda builds on existing models and discussions in related domains, structures open questions and provides guidance for IS researchers and practitioners in the face of ongoing digital gullibility

Application of the Benford’s law to Social bots and Information Operations activities

Benford\u27s law shows the pattern of behavior in normal systems. It states that in natural systems digits\u27 frequency have a certain pattern such that the occurrence of first digits in numbers are unevenly distributed. In systems with natural behavior, numbers begin with a “1” are more common than numbers beginning with “9”. It implies that if the distribution of first digits deviate from the expected distribution, it is indicative of fraud. It has many applications in forensic accounting, stock markets, finding abnormal data in survey data, and natural science. We investigate whether social media bots and Information Operations activities are conformant to the Benford\u27s law. Our results showed that bots\u27 behavior adhere to Benford\u27s law, suggesting that using this law helps in detecting malicious online automated accounts and their activities on social media. However, activities related to Information Operations did not show consistency in regards to Benford\u27s law. Our findings shedlight on the importance of examining regular and anomalous online behavior to avoid malicious and contaminated content on social media

Two-Sided Matching for mentor-mentee allocations—Algorithms and manipulation strategies

In scenarios where allocations are determined by participant’s preferences, Two-Sided Matching is a well-established approach with applications in College Admissions, School Choice, and Mentor-Mentee matching problems. In such a context, participants in the matching have preferences with whom they want to be matched with. This article studies two important concepts in Two-Sided Matching: multiple objectives when finding a solution, and manipulation of preferences by participants. We use real data sets from a Mentor-Mentee program for the evaluation to provide insight on realistic effects and implications of the two concepts. In the first part of the article, we consider the quality of solutions found by different algorithms using a variety of solution criteria. Most current algorithms focus on one criterion (number of participants matched), while not directly taking into account additional objectives. Hence, we evaluate different algorithms, including multi-objective heuristics, and the resulting trade-offs. The evaluation shows that existing algorithms for the considered problem sizes perform similarly well and close to the optimal solution, yet multi-objective heuristics provide the additional benefit of yielding solutions with better quality on multiple criteria. In the second part, we consider the effects of different types of preference manipulation on the participants and the overall solution. Preference manipulation is a concept that is well established in theory, yet its practical effects on the participants and the solution quality are less clear. Hence, we evaluate the effects of three manipulation strategies on the participants and the overall solution quality, and investigate if the effects depend on the used solution algorithm as well

Do We Choose What We Desire? – Persuading Citizens to Make Consistent and Sustainable Mobility Decisions

A dilemma in urban mobility with tremendous effects on citizens’ wellbeing is the unconscious antipode between their short- and long-term goals. People do not anticipate all consequences of their modal choices and thus make decisions that might be incoherent with their desires, e.g. taking their own car due to convenience but causing a congested city. Omnipresent Information Systems on smartphones provide the necessary information and coordination capabilities to support people for sustainable and individually coherent mobility decisions on a mass scale. Building upon extant work in travel behavior and social psychology, a framework is proposed to coordinate research efforts in the development of persuading measures for sustainable mobility decisions. This framework accounts for user heterogeneity, motivation and wellbeing as influential dimensions in the mobility decision process. Tied to social influence the derived measures contribute to a behavioral change in people’s mobility behavior leading to a higher wellbeing level in urban areas

Visualization, Feature Selection, Machine Learning: Identifying the Responsible Group for Extreme Acts of Violence

The toll of human casualties and psychological impacts on societies make any study on violent extremism worthwhile, let alone attempting to detect patterns among them. This paper is an effort to predict which violent extremist organization (VEO), among 14 currently active ones throughout the world, is responsible for a violent act based on 14 features, including its human and structural tolls, its target type and value, intelligence, and weapons utilized in the attack. Three main steps in our paper include: 1) the visualization of the violent acts through linear and non-linear dimensionality reduction techniques; 2) sequential forward feature selection based on the generalization accuracy of three machine learning models–decision tree, and linear and nonlinear SVM; and 3) employing multilayer perceptron to predict the VEO based on the selected features of a violent act. Top-ranked selected features were related to the target type and plan and the multilayer perceptron achieved up to 40% test accuracy