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Location-based games (LBGs) have afforded novel information technology (IT) developments in how people interact with the physical world. Namely, LBGs have spurred a wave of territoriality (i.e. controlling) and exploration (i.e. discovering) of augmented physical space that are driven by different social dynamics related to group formation, social connectivity and altruism. The aim of this study is to investigate this dynamic and how it is further related to the use intensity of location-based IT.

This work presents a structural equation model that connects social dimensions of play to territorial control and exploration, and playing intensity. The model was tested with psychometric data gathered from a global sample of Pokémon GO players (N = 515).

In the tested sample, players' social self-efficacy and altruism were positively associated with team identification. Team identification, in turn, was positively associated with both territorial control and exploration tendency. Territorial control had a significant relationship with playing intensity; however, exploration tendency did not. This implies territorial control is the stronger predictor of playing intensity.

The findings suggest that human primal urges to conquer and control geographical territory may surface in the digital reimagination of physical space. LBGs offer opportunities for making use of new forms of play (territorial control and exploration) in motivating locative behaviours.

This research quantifies the relationships between a social predisposition, team identification, territorial control, exploration tendency and playing intensity in the context of Pokémon GO. It contributes new knowledge to the understanding of territorial behaviour (control and exploration) in location-based IT.

This study investigates how game design, which divides players into static teams, can reinforce group polarisation. The authors study this phenomenon from the perspective of social identity in the context of team-based location-based games, with a focus on game slang.

The authors performed an exploratory data analysis on an original dataset of n = 242,852 messages from five communication channels to find differences in game slang adoption between three teams in the location-based augmented reality game Pokémon GO. A divisive word “jym” (i.e. a Finnish slang derivative of the word “gym”) was discovered, and players' attitudes towards the word were further probed with a survey (n = 185). Finally, selected participants (n = 25) were interviewed in person to discover any underlying reasons for the observed polarised attitudes.

The players' teams were correlated with attitudes towards “jym”. Face-to-face interviews revealed association of the word to a particular player subgroup and it being used with improper grammar as reasons for the observed negative attitudes. Conflict over (virtual) territorial resources reinforced the polarisation.

Game design with static teams and inter-team conflict influences players' social and linguistic identity, which subsequently may result in divisive stratification among otherwise cooperative or friendly player-base.

The presented multi-method study connecting linguistic and social stratification is a novel approach to gaining insight on human social interactions, polarisation and group behaviour in the context of location-based games.

Wearable health technologies (WHTs) show promise in improving the health and well-being of the aging population because they promote healthy lifestyles. They can be used to collect health information from users and encourage them to be physically active. Despite potential benefits of WHTs, recent studies have shown that older people have low continued use intention toward WHTs. Previous work on this topic is disjointed, and new theoretical viewpoints are required.

The authors propose an enablers and inhibitors perspective to model factors influencing continued use intention of WHTs among the elderly. To test the model, we collected data from Chinese elderly (N = 295) who had prior experience using WHTs.

The study results show that social value is the strongest enabler of continued WHT use, and emotional and epistemic values and device quality also increase use continuance. Inertia and technology anxiety were identified as significant inhibitors. A post hoc importance performance map analysis revealed that while emotional value is a highly significant predictor of continued WHT use, existing WHTs do not stimulate such value in our sample.

The research findings illustrate the importance of incorporating user resistance in technology acceptance studies in general and WHT usage studies in particular. This study contributes by providing an integrative model of technology continued use intention for the elderly along with practical implications for policymakers.

A limited number of prior studies have taken both enablers and inhibitors into account when explaining continued WHT use intention among the elderly. This paper fills this research gap and contributes to the WHT literature by considering both enablers and inhibitors in the same model. Moreover, this study contributes to the ongoing research on WHT, and more broadly, gerontechnology use among the elderly.

Inscrutable machine learning (ML) models are part of increasingly many information systems. Understanding how these models behave, and what their output is based on, is a challenge for developers let alone non-technical end users.

The authors investigate how AI systems and their decisions ought to be explained for end users through a systematic literature review.

The authors’ synthesis of the literature suggests that AI system communication for end users has five high-level goals: (1) understandability, (2) trustworthiness, (3) transparency, (4) controllability and (5) fairness. The authors identified several design recommendations, such as offering personalized and on-demand explanations and focusing on the explainability of key functionalities instead of aiming to explain the whole system. There exists multiple trade-offs in AI system explanations, and there is no single best solution that fits all cases.

Based on the synthesis, the authors provide a design framework for explaining AI systems to end users. The study contributes to the work on AI governance by suggesting guidelines on how to make AI systems more understandable, fair, trustworthy, controllable and transparent.

This literature review brings together the literature on AI system communication and explainable AI (XAI) for end users. Building on previous academic literature on the topic, it provides synthesized insights, design recommendations and future research agenda.