Yan Liang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Yan Liang has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Haojie Tu
Business School, Southwest Minzu University, Chengdu 610041, China
1 shared publication - Huicong Hu
Department of Design, Harbin Institute of Technology (Shenzhen), Shenzhen 518000, China
1 shared publication - Irena Spasić
School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom of Great Britain and Northern Ireland
1 shared publication - Liangjie Zhao
Business School, Southwest Minzu University, Chengdu 610041, China
1 shared publication - Yanzhang Tong
Department of Mechanical Engineering, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
1 shared publication - Ying Liu
Department of Mechanical Engineering, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
1 shared publication
Latest Publications
- Open Access
How Do Clusters Drive Firm Performance in the Regional Innovation System? A Causal Complexity Analysis in Chinese Strategic Emerging Industries
From a configurational perspective, this study aims to explain how clusters drive firm performance in the regional innovation system by considering the relationship between cluster and firm performance as causal complexity. Using an original dataset comprising 292 Chinese firms in strategic emerging industries (SEIs), this study employs a fuzzy set qualitative comparative analysis (fsQCA) to investigate the conjunction effects of interorganizational dependence, network embeddedness, and ambidextrous innovation on cluster firms’ performance. The results showed that the fsQCA method uncovers causal combinations of these cluster factors that lead to high performance. These configurations imply two alternative pathways to enhance performance, where exploitative innovation is identified as the core causal condition.
Open AccessHow Do Clusters Drive Firm Performance in the Regional Innovation System? A Causal Complexity Analysis in Chinese Strategic Emerging Industries
From a configurational perspective, this study aims to explain how clusters drive firm performance in the regional innovation system by considering the relationship between cluster and firm performance as causal complexity. Using an original dataset comprising 292 Chinese firms in strategic emerging industries (SEIs), this study employs a fuzzy set qualitative comparative analysis (fsQCA) to investigate the conjunction effects of interorganizational dependence, network embeddedness, and ambidextrous innovation on cluster firms’ performance. The results showed that the fsQCA method uncovers causal combinations of these cluster factors that lead to high performance. These configurations imply two alternative pathways to enhance performance, where exploitative innovation is identified as the core causal condition.
- 10 May 2022
- Published by Wiley in Journal of Computing and Information Science in Engineering
- Vol. 22 (6)
A Data-Driven Approach for Integrating Hedonic Quality and Pragmatic Quality in User Experience Modeling
User experience (UX) analysis is essential for designers and companies when optimizing products or services as it can help designers to uncover valuable information, such as the hedonic and pragmatic qualities of a UX. While previous research has described the conventional methods of UX analysis, such as surveys or subjective determination, this paper proposes a data-driven methodology to automatically integrate hedonic and pragmatic qualities for UX from online customer reviews. The proposed methodology comprises the following steps. First, we combined a corpus-based approach, a dictionary-based approach and word embedding to generate a lexicon of hedonic and pragmatic qualities. Second, we filtered out the sentences that contained no hedonic or pragmatic information and classified the remaining review sentences. Third, we extracted and clustered the UX elements (such as product feature, context information and context clustering). Finally, we scored each UX element based on hedonic or pragmatic qualities and compared it against previous UX modeling. This study integrates hedonic and pragmatic qualities to enrich UX modeling in the field of UX. For a product designer, the UX analysis results may highlight a requirement to optimize product design. It may also represent a potential market opportunity in a UX state where most of the current products are perceived UX results by customers. This research also examines the invaluable relationship between UX and online customer reviews to support the prospective planning of customer strategy and design activities.
- 10 May 2022
- Published by Wiley in Journal of Computing and Information Science in Engineering
- Vol. 22 (6)
A Data-Driven Approach for Integrating Hedonic Quality and Pragmatic Quality in User Experience Modeling
User experience (UX) analysis is essential for designers and companies when optimizing products or services as it can help designers to uncover valuable information, such as the hedonic and pragmatic qualities of a UX. While previous research has described the conventional methods of UX analysis, such as surveys or subjective determination, this paper proposes a data-driven methodology to automatically integrate hedonic and pragmatic qualities for UX from online customer reviews. The proposed methodology comprises the following steps. First, we combined a corpus-based approach, a dictionary-based approach and word embedding to generate a lexicon of hedonic and pragmatic qualities. Second, we filtered out the sentences that contained no hedonic or pragmatic information and classified the remaining review sentences. Third, we extracted and clustered the UX elements (such as product feature, context information and context clustering). Finally, we scored each UX element based on hedonic or pragmatic qualities and compared it against previous UX modeling. This study integrates hedonic and pragmatic qualities to enrich UX modeling in the field of UX. For a product designer, the UX analysis results may highlight a requirement to optimize product design. It may also represent a potential market opportunity in a UX state where most of the current products are perceived UX results by customers. This research also examines the invaluable relationship between UX and online customer reviews to support the prospective planning of customer strategy and design activities.