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As the application of artificial intelligence (AI) becomes more prevalent, many high-tech firms have employed AI applications to deal with emerging societal, technological and…
Abstract
Purpose
As the application of artificial intelligence (AI) becomes more prevalent, many high-tech firms have employed AI applications to deal with emerging societal, technological and environmental challenges. Big data analytical capability (BDAC) has become increasingly important in the AI application processes. Drawing upon the resource-based view and the theory of planned behavior, this study aims to investigate how BDAC and collaboration affect new product performance (NPP). Practically, a harmonic working team is particularly important for creating management synergies, this empirical analysis demonstrates the importance of BDAC and collaboration for NPP.
Design/methodology/approach
This paper focuses on the performance of firms that applied AI in their operations. This study collected data from firms in Greater China, including China and Taiwan, as Greater China is currently the leading manufacturer of semiconductor, electronic and electric products for AI applications in the manufacturing process. Confirmatory factor analysis and structural equation modeling is employed for statistical analysis.
Findings
The analytical results indicate that BDAC positively relates to collaboration capability (CC) in AI applications but not to team collaboration (TC). CC positively correlates with TC, and both CC and TC positively correlate with NPP. Further, the mediating effect was examined using the Sobel t-test, which reveals that CC is a significant mediator in the influence of BDAC on NPP.
Practical implications
The strategic implementation of BDAC and collaboration can allow an enterprise to improve its NPP when driven by the external environment to use AI, which further enhances NPP. These processes indicate that AI and BDAC are both crucial for the success of a company’s collaboration and for effective management to improve NPP in the face of global competition.
Originality/value
This study introduces the concept of BDAC to explain the relationship between CC and TC, as they pertain to NPP. This study presented a discussion of the theoretical and practical implications of the research findings and could provide a framework for managing BDAC.
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Wei Yang, Luu Quoc Phong, Tracy-Anne De Silva and Jemma Penelope
This study aims to understand New Zealand sheep farmers’ readiness toward sustainability transition by assessing their intentions of transition and adoption of sustainability…
Abstract
Purpose
This study aims to understand New Zealand sheep farmers’ readiness toward sustainability transition by assessing their intentions of transition and adoption of sustainability tools, with information collection considered to mediate the intention–adoption relationship.
Design/methodology/approach
Based on the data collected from a survey of New Zealand sheep farmers in 2021, the empirical analysis was developed to investigate farmers’ perceptions of and attitudes toward readiness to move toward a sustainability transition. Structural equation modeling associated with principal component analysis was used to empirically test the theory of planned behavior constructs.
Findings
The results show that pressure from the public and the sheep industry, and the perceived controls of transition drive the intention of sustainability transition; farmers with higher intention of sustainability transition are found to be more likely to adopt sustainability tools. However, there is an attitude–behavior gap, wherein positive attitudes toward sustainability transition may not lead to a higher likelihood of adopting sustainability tools. There is no evidence of the mediating role of information collection on the intention–adoption relationship, while a positive effect was found in information collection on the adoption of sustainability tools.
Practical implications
The empirical evidence indicates that policymakers need to help increase the awareness of sustainable production and help farmers overcome barriers to achieving sustainable production by finding ways to turn intentions into adoption.
Originality/value
Being the first attempt to empirically assess farmers’ readiness toward sustainability transition, the study fills the gap of limited understanding of the link between sustainability transition intention and sustainable tools adoption in sustainability transition.
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Unstructured data such as images have defied usage in property valuation for a long time. Instead, structured data in tabular format are commonly employed to estimate property…
Abstract
Purpose
Unstructured data such as images have defied usage in property valuation for a long time. Instead, structured data in tabular format are commonly employed to estimate property prices. This study attempts to quantify the shape of land lots and uses the resultant output as an input variable for subsequent land valuation models.
Design/methodology/approach
Imagery data containing land lot shapes are fed into a convolutional neural network, and the shape of land lots is classified into two categories, regular and irregular-shaped. Then, the intermediate output (regularity score) is utilized in four downstream models to estimate land prices: random forest, gradient boosting, support vector machine and regression models.
Findings
Quantification of the land lot shapes and their exploitation in valuation led to an improvement in the predictive accuracy for all subsequent models.
Originality/value
The study findings are expected to promote the adoption of elusive price determinants such as the shape of a land lot, appearance of a house and the landscape of a neighborhood in property appraisal practices.
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