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Training system for designing computer games for early career guidance of schoolchildren (Junior Skills)
The article deals with the development of a training system for the design of computer games based on the use of information technologies for early vocational training and vocational guidance of schoolchildren (Junior Skills). The peculiarity of vocational guidance work with schoolchildren is primarily associated with the need to overcome the contradictions between the state of the labor market, the needs of the economy and the subjective professional preferences of young people. The solution to youth career guidance issues is offered in the Junior Skills project, which includes students, employer representatives, and the expert community.
Journal Article
Chinese martial arts and media culture : global perspectives
Signs and images of Chinese martial arts increasingly circulate through global media cultures. As tropes of martial arts are not restricted to what is considered one medium, one region, or one (sub)genre, the essays in this collection are looking across and beyond these alleged borders. From 1920s wuxia cinema to the computer game cultures of the information age, they trace the continuities and transformations of martial arts and media culture across time, space, and multiple media platforms.
Book
Grandmaster level in StarCraft II using multi-agent reinforcement learning
Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal, the domain of StarCraft has emerged as an important challenge for artificial intelligence research, owing to its iconic and enduring status among the most difficult professional esports and its relevance to the real world in terms of its raw complexity and multi-agent challenges. Over the course of a decade and numerous competitions
1
–
3
, the strongest agents have simplified important aspects of the game, utilized superhuman capabilities, or employed hand-crafted sub-systems
4
. Despite these advantages, no previous agent has come close to matching the overall skill of top StarCraft players. We chose to address the challenge of StarCraft using general-purpose learning methods that are in principle applicable to other complex domains: a multi-agent reinforcement learning algorithm that uses data from both human and agent games within a diverse league of continually adapting strategies and counter-strategies, each represented by deep neural networks
5
,
6
. We evaluated our agent, AlphaStar, in the full game of StarCraft II, through a series of online games against human players. AlphaStar was rated at Grandmaster level for all three StarCraft races and above 99.8% of officially ranked human players.
AlphaStar uses a multi-agent reinforcement learning algorithm and has reached Grandmaster level, ranking among the top 0.2% of human players for the real-time strategy game StarCraft II.
Journal Article
Gamification in the Workplace: The Central Role of the Aesthetic Experience
Although gamification in the workplace is burgeoning, organizations frequently have difficulty sustaining user engagement with a gamified information system (IS). The focus of this study is how a gamified IS in the workplace engages users and encourages them to continue system use. By proposing the concepts of flow experience (FE) and aesthetic experience (AE) as different ways to provide deep and meaningful user engagement, this study develops a model that explores the antecedents of FE and AE and their roles in explaining an individual's continuance intention to use of a gamified IS. The model is tested using data collected from 178 users of a gamified IS in a global consulting company. The results demonstrate that although FE and AE are complementary forces, AE is more salient than FE for explaining continuance intention. The research proposes AE as a parsimonious yet powerful construct that extends the research on user engagement. The findings contribute to research on gamification by shifting scholarly attention from deep engagement characterized by FE to meaningful engagement characterized by AE.
Journal Article
Demand Heterogeneity in Platform Markets: Implications for Complementors
While two-sided platforms (e.g., video game consoles) depend on complements (e.g., games) for their success, the success of complements is also influenced by platform-level dynamics. Research suggests that greater platform adoption benefits complements by providing more potential users, but this assumes that platform adopters are homogeneous. We build on extensive research exploring the heterogeneity between early and late platform adopters to identify counterintuitive dynamics for complements. Complements launched early in a platform’s life cycle face an audience entirely of early platform adopters, whereas later-launching complements face a mixed audience of both early and late adopters, and we argue that differences in preferences and behavior between early and late adopters affect whether complements will succeed and which types will be most successful. We explore these dynamics in the context of the console video game industry using a unique data set of 2,918 video games released in the United Kingdom from 2000 to 2007. We show that despite the increase in the potential user pool as the platform evolves, video games launched later in the platform life cycle realize lower sales than those launched earlier. While increased competition explains part of this effect, we show substantial evidence consistent with our theory of preference differences between early and late adopters. This includes the finding that the negative effect is stronger for novel games and that the gap between popular and less popular complements widens as later adopters move into the platform, consistent with late adopters being risk averse and seeking to avoid purchasing mistakes.
The e-companion is available at
https://doi.org/10.1287/orsc.2017.1183
.
Journal Article
What drives digital engagement with sponsored videos? An investigation of video influencers’ authenticity management strategies
Sponsored videos have rapidly emerged as an important marketing tool as video sharing platforms and the popularity of video influencers have grown. However, little research explores how sponsored videos’ design strategies affect viewer engagement. Using field data, this study highlights influencers’ authenticity dilemma in sponsored video design and tests which features drive digital engagement. Specifically, this study conceptualizes and empirically tests a comprehensive framework, involving passion- and transparency-based strategies as well as platform- and brand-factors, to determine how influencers can best manage the authenticity dilemma. Results show that explicitly disclosing brand sponsorship, alone, and in combination with platform-generated disclosure, positively impacts digital engagement, indicating an evolution in consumer persuasion knowledge. Early brand appearance, high video customization, and influencers’ subjective endorsements, such as sharing personal experiences or opinions about the sponsored product, impair sponsored videos’ digital engagement. In addition to contributing theoretical insights on authenticity management strategies and sponsorship disclosure in influencer videos, this research offers practical recommendations to influencers on how to design more engaging sponsored videos.
Journal Article
Platform Architecture and Quality Trade-offs of Multihoming Complements
Multihoming, the decision to design a complement to operate on multiple platforms, is becoming increasingly common in many platform markets. Perceived wisdom suggests that multihoming is beneficial for complement providers as they expand their market reach, but it reduces differentiation among competing platforms as the same complements become available on different platforms. We argue that complement providers face trade-offs when designing their products for multiple platform architectures—they must decide how far to specialize the complement to each platform’s technological specifications. Because of these trade-offs, multihoming complements can have different quality performance across platforms. In a study of the U.S. video game industry, we find that multihoming games have lower-quality performance on a technologically more complex console than on a less complex one. Also, games designed for and released on a focal platform have lower-quality performance on platforms they are subsequently multihomed to. However, games that are released on the complex platform with a delay suffer a smaller drop in quality on complex platforms. This has important implications for platform competition, and for managers considering expanding their reach through multihoming.
The online appendix is available at
https://doi.org/10.1287/isre.2018.0779
.
Journal Article
Anthropomorphized Helpers Undermine Autonomy and Enjoyment in Computer Games
Although digital assistants with humanlike features have become prevalent in computer games, few marketing studies have demonstrated the psychological mechanisms underlying consumers’ reactions to digital assistants and their subsequent influence on consumers’ game enjoyment. To fill this gap, the current study examined the effect of anthropomorphic representations of computerized helpers in computer games on game enjoyment. In the current research, consumers enjoyed a computer game less when they received assistance from a computerized helper imbued with humanlike features than from a helper construed as a mindless entity. We offer a novel mechanism that the presence of an anthropomorphized helper can undermine individuals’ perceived autonomy during a computer game. Across six experiments, we show that the presence of an anthropomorphized helper reduced game enjoyment across three different games. By measuring participants’ perceived autonomy (study 1) and employing moderators such as importance of autonomy (studies 2, 3, and 4), we also provide evidence that the reduced feeling of autonomy serves as the mechanism underlying the backfiring effect. Finally, we demonstrate that the effect of anthropomorphism on game enjoyment can be extended to other game-related outcomes, such as individuals’ motivation to persist in the game (studies 4 and 5).
Journal Article
Pixel-Wise Crowd Understanding via Synthetic Data
Crowd analysis via computer vision techniques is an important topic in the field of video surveillance, which has wide-spread applications including crowd monitoring, public safety, space design and so on. Pixel-wise crowd understanding is the most fundamental task in crowd analysis because of its finer results for video sequences or still images than other analysis tasks. Unfortunately, pixel-level understanding needs a large amount of labeled training data. Annotating them is an expensive work, which causes that current crowd datasets are small. As a result, most algorithms suffer from over-fitting to varying degrees. In this paper, take crowd counting and segmentation as examples from the pixel-wise crowd understanding, we attempt to remedy these problems from two aspects, namely data and methodology. Firstly, we develop a free data collector and labeler to generate synthetic and labeled crowd scenes in a computer game, Grand Theft Auto V. Then we use it to construct a large-scale, diverse synthetic crowd dataset, which is named as “GCC Dataset”. Secondly, we propose two simple methods to improve the performance of crowd understanding via exploiting the synthetic data. To be specific, (1) supervised crowd understanding: pre-train a crowd analysis model on the synthetic data, then fine-tune it using the real data and labels, which makes the model perform better on the real world; (2) crowd understanding via domain adaptation: translate the synthetic data to photo-realistic images, then train the model on translated data and labels. As a result, the trained model works well in real crowd scenes.Extensive experiments verify that the supervision algorithm outperforms the state-of-the-art performance on four real datasets: UCF_CC_50, UCF-QNRF, and Shanghai Tech Part A/B Dataset. The above results show the effectiveness, values of synthetic GCC for the pixel-wise crowd understanding. The tools of collecting/labeling data, the proposed synthetic dataset and the source code for counting models are available at https://gjy3035.github.io/GCC-CL/.
Journal Article