State of the art in business games

The use of digital games and gamification has demonstrated potential to improve many aspects of how businesses provide training to staff, and communicate with consumers. However, there is still a need for better understanding of how the adoption of games and gasification would influence the process of decision-making in organisations across different industry. This article provides a structured review of existing literature on the use of games in the business environment, and seeks to consolidate findings to address research questions regarding their perception, proven efficacy, and identifies key areas for future work. The findings highlight that serious games can have positive and effective impacts in multiple areas of a business, including training, decision-support, and consumer outreach. They also emphasise the challenges and pitfalls of applying serious games and gamification principles within a business context, and discuss the implications of development and evaluation methodologies on the success of a game-based solution.

Exploiting heterogeneous scientific literature networks to combat ranking bias:evidence from the computational linguistics area

It is important to help researchers find valuable papers from a large literature collection. To this end, many graph-based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph-based ranking algorithm, MutualRank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less-biased ranking than previous methods. MutualRank provides a unified model that involves both intra- and inter-network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well-known universities and two well-known textbooks. The experimental results show that MutualRank greatly outperforms the state-of-the-art competitors, including PageRank, HITS, CoRank, Future Rank, and P-Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by MutualRank are also quite reasonable.