Konstantinos Papangelis

Muhammad Raees, Konstantinos Papangelis, Vassilis Javed Khan

In sales and marketing, customer segmentation is an important tool for formulating strategies for customer treatment and supply chain management. Most segmentation implementations rely on limited criteria, such as recency, frequency, and monetary (RFM) modeling, which often fail to capture complex business interactions. In this work, we design and evaluate a dynamic multi-criteria decision-making (MCDM) method in a business-to-business (B2B) manufacturing context by 1) extending RFM to dimensions of stability and growth, 2) integrating an adaptive and analytical hierarchical process to match business objectives, and 3) evaluating multivariate time-series clustering models. We then measure customer stability, tracking between-segment transitions, and volatility over time, and apply a graph-based consensus model to further strengthen the analysis. We test the efficacy of the proposed method using a real-world manufacturing company dataset to segment more than 3,000 B2B customers, showing strong robustness to temporal shifts. The implementation enables domain experts with preferential analytics to devise their strategies, providing effective decision support for B2B customer segmentation.

Muhammad Raees, Konstantinos Papangelis

While human-AI decision-making research has primarily used trust measurements to assess the practical usage of AI systems by their end-users, recent empirical evidence suggests that trust measurements do not inform users' appropriate reliance on AI systems. While examining the human-AI decision-making literature, in this work, we review empirical studies that assess people's appropriate reliance on AI advice, differentiating measurements and constructs of appropriate reliance from trust and mere reliance. Our analysis of literature shows that constructs for human-AI appropriate reliance are still fragmented in research. We present three views on appropriate reliance, namely Traditional, Appropriateness, and Dominance, as discussed in research. Using these views, we evaluate objective metrics reported in studies and argue for their consensus to facilitate the comparison across empirical research. We also discuss how studies employ objective metrics and examine their validity in application contexts. Our work contributes to the critical body of research on exploring objective metrics for assessing humans' appropriate reliance on AI advice.

John Dunham, Konstantinos Papangelis, Samuli Laato et al.

Since its creation, the Location-Based Game (LBG), Pokémon GO, has been embraced by a community of fans across the world. Due to its recency, the impact of COVID-19 on the community of Pokémon GO players is underexplored. We address how COVID-19 has impacted the players of Pokémon GO by building upon existing work focusing on player gratifications and impacts in Pokémon GO. Through semi-structured interviews, we provide a snapshot of the state of LBG play during unprecedented times. These player testimonies demonstrate (1) the importance of in-person socialization to LBG, (2) additional ways players use the game as a coping mechanism, and (3) how intentionality mediates player perceptions of people-place relationships. In demonstrating these behaviors, we provide a glimpse of how a game that forces players to explore the world around them changed when going outside with friends became a source of danger.

John Dunham, Konstantinos Papangelis, Nicolas LaLone et al.

Location-based games (LBG) impose virtual spaces on top of physical locations. Studies have explored LBG from various perspectives. However, a comprehensive study of who these players are, their traits, their gratifications, and the links between them is conspicuously absent from the literature. In this paper, we aim to address this lacuna through a series of surveys with 2390 active LBG players utilizing Tondello's Player Traits Model and Scale of Game playing Preferences, and Hamari's scale of LBG gratifications. Our findings (1) illustrate an association between player satisfaction and social aspects of the studied games, (2) explicate how the core-loops of the studied games impact the expressed gratifications and the affine traits of players, and (3) indicate a strong distinction between hardcore and casual players based on both traits and gratifications. Overall our findings shed light into the players of LBG, their traits, and gratifications they derive from playing LBGs.

Ioanna Lykourentzou, Federica Lucia Vinella, Faez Ahmed et al.

As the volume and complexity of distributed online work increases, the collaboration among people who have never worked together in the past is becoming increasingly necessary. Recent research has proposed algorithms to maximize the performance of such teams by grouping workers according to a set of predefined decision criteria. This approach micro-manages workers, who have no say in the team formation process. Depriving users of control over who they will work with stifles creativity, causes psychological discomfort and results in less-than-optimal collaboration results. In this work, we propose an alternative model, called Self-Organizing Teams (SOTs), which relies on the crowd of online workers itself to organize into effective teams. Supported but not guided by an algorithm, SOTs are a new human-centered computational structure, which enables participants to control, correct and guide the output of their collaboration as a collective. Experimental results, comparing SOTs to two benchmarks that do not offer user agency over the collaboration, reveal that participants in the SOTs condition produce results of higher quality and report higher teamwork satisfaction. We also find that, similarly to machine learning-based self-organization, human SOTs exhibit emergent collective properties, including the presence of an objective function and the tendency to form more distinct clusters of compatible teammates.

Irwyn Sadien, Konstantinos Papangelis, Charles Fleming et al.

Research in Mobile Location-Based Crowdsourcing is hindered by a marked lack of real-world data. The development of a standardized, lightweight, easily deployable, modular, composable, and most of all, scalable experimentation framework would go a long way in facilitating such research. Conveniently, these are all salient characteristics of systems developed using a microservices approach. We propose QRowdsource - a MLBC experimentation framework built using a distributed services architecture. In this paper, we discuss the design and development of QRowdsource, from the decomposition of functional components to the orchestration of services within the framework. We also take a look at how the advantages and disadvantages of using a microservices approach translate to our specific use case and deliberate over a number of lessons learned while developing the experimentation framework.