Users' subsequent innovation after organizational adoption: evidence from an online game user innovation community

Li, Weimo; Lu, Yaobin; Ma, Jifeng; Wang, Bin

doi:10.1108/intr-08-2021-0578

Cited by 10 publications

(4 citation statements)

References 72 publications

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“…New product development in the games industry is usually a long cycle and requires a lot of human and financial capital, while the needs of gamers are changing fast. In order to better meet the requirements of gamers, many game development firms normally practice open innovation by hosting a user innovation community to leverage external sources of innovation (Hau and Kim, 2011; Hsiao and Chiou, 2012; Ma et al , 2019; Li et al , 2023).…”

Section: Methodsmentioning

confidence: 99%

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Network constraint and online group innovation: a dual perspective of inter-community group network and intra-community group network

Liu,

Zhu,

Wei

2024

IMDS

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PurposeThis study aims to explore the different effects of inter-community group networks and intra-community group networks on group innovation.Design/methodology/approachThe authors used a pooled panel dataset of 12,111 self-organizing innovation groups in 463 game product creative workshop communities from Steam support to test the hypothesis. The pooled ordinary least squares (OLS) model is used for analyzing the data.FindingsThe results show that network constraint is negatively associated with the innovation performance of online groups. The average path length of the inter-community group network negatively moderates the relationship between network constraint and group innovation, while the average path length of the intra-community group network positively moderates the relationship between network constraint and group innovation. In addition, both the network density of inter-community group networks and intra-community group networks can negatively moderate the negative relationship between network constraint and group innovation.Originality/valueThe findings of this study suggest that network structural characteristics of inter-community networks and intra-community networks have different effects on online groups’ product innovation, and therefore, group members should consider their inter- and intra-community connections when choosing other groups to form a collaborative innovation relationship.

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Section: Methodsmentioning

confidence: 99%

“…In order to better meet the requirements of gamers, many game development firms normally practice open innovation by hosting a user innovation community to leverage external sources of innovation (Hau and Kim, 2011;Hsiao and Chiou, 2012;Ma et al, 2019;Li et al, 2023).…”

Section: Research Context and Samplingmentioning

confidence: 99%

Network constraint and online group innovation: a dual perspective of inter-community group network and intra-community group network

Liu,

Zhu,

Wei

2024

IMDS

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“…This limitation is due to methodological and perspective constraints, leading to a less in-depth analysis of the role of innovation in driving green development. The academic contribution and unique features of this paper lie in the following aspects: (1) The use of grey comprehensive evaluation to select indicators that best represent innovation quality, thus providing a clear definition of regression independent variables; (2) To mitigate multicollinearity among variables, a principal component approach is applied, considering four dimensions: technology-driven, industry-driven, policy-driven, and market-driven factors [3][4][5][6][7]. The analysis of multiple linear regression results, followed by back substitution [8][9][10]; (3) A comprehensive examination of the driving factors behind insufficient innovation quality within the framework of green development, which allows for drawing conclusions and proposing corresponding policy recommendations.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on the Driving Factors of Regional Innovation Quality Based on Multiple Linear Regression Model

Wang,

2024

Proceedings of the 5th International Conference on Economic Management and Model Engineering, ICEMME 2023, November 17–19, 2023

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The efficiency of urban green development is deeply rooted in the innovation capabilities of urban clusters. Adhering to innovation-driven strategies to lead green development enhances the innovation quality of urban clusters. This study employs grey comprehensive evaluation to select indicators that represent innovation quality and establishes a principal component regression model for researching the driving factors of innovation quality in the Yangtze River Delta urban cluster. Empirical research is conducted using data related to the overall green innovation quality driving factors for 41 cities in the Yangtze River Delta from 2010 to 2020. The study indicates that the regional gross domestic product (GDP) is the most representative indicator of innovation quality. Factors such as technological research and development, the number of patents, economic foundations, and business development exhibit significant positive effects, while environmental planning, management systems, human capital, and international openness show significant negative effects. To enhance the innovation quality in the Yangtze River Delta, it is essential to address the deficiencies in policy and market-driven approaches. Emphasizing green, high-quality development, taking into consideration both economic and ecological benefits, enhancing technological innovation capabilities, and aligning expansion of business plans with environmental planning are key strategies. Additionally, optimizing the structure and quality of higher education, making sensible investments in human capital, and focusing on the quality of foreign capital inflow are crucial steps towards achieving this goal.

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“…With the wide application of Internet technology in the world, the number of users/terminals of all kinds has already exceeded one billion, and all fields have entered the informationization era to a certain extent [1][2][3]. However, as computer network technology continues to develop, the user scale continues to expand at the same time, accompanied by all kinds of user/terminal abnormal behavior affecting the normal operation of the network and the increasingly heavy network operation and maintenance, analysis work [4][5].…”

Section: Introductionmentioning

confidence: 99%

Abnormal User Behavior Detection for Industry Big Data Analysis

Wang,

2024

Applied Mathematics and Nonlinear Sciences

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The security of user information and the precision of user services are paramount, necessitating effective detection of abnormal user behavior in vast datasets. This study introduces the QGAN-BDE algorithm, which leverages a quantum generative adversarial network combined with a novel approach for detecting and evaluating abnormal user behavior. Through a feature matching strategy, the algorithm ensures close data alignment between the generator and discriminator. At the same time, the integration of a classical convolutional neural network within the BDE network assesses user behavior abnormalities. Setting distinct thresholds for abnormal behavior and threats enables the differentiation between normal and abnormal activities. Utilizing a dataset and financial stock log data for simulation, the proposed method achieves an AUC value of approximately 0.912 with small negative data samples. Additionally, it records generator and discriminator loss values within the ranges of [1.05,1.55] and [0.49,0.61], respectively, and demonstrates over 80% accuracy in detecting financial stock log anomalies. This method’s reliance on comprehensive big data allows for an in-depth analysis of user behavior, facilitating the timely identification and management of abnormalities.

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