计划行为理论
独创性
价值(数学)
概念模型
规范(哲学)
营销
心理学
产品(数学)
业务
社会心理学
经济
控制(管理)
政治学
计算机科学
管理
几何学
数学
数据库
机器学习
创造力
法学
作者
Zhenzong Zhou,Geoffrey Qiping Shen,Jin Xue,Chuang Sun,Yongyue Liu,Weiyi Cong,Tao Yu,Yaowu Wang
出处
期刊:Engineering, Construction and Architectural Management
[Emerald (MCB UP)]
日期:2023-10-31
标识
DOI:10.1108/ecam-05-2023-0473
摘要
Purpose This study aims to develop an improved understanding of the formation of citizens' purchase intention to increase the adoption of prefabricated housing (PH). Design/methodology/approach An integrative model of the theory of planned behavior (TPB) and norm activation model (NAM) was proposed based on previous studies. To verify the conceptual model, an analysis was conducted after data collection from a questionnaire survey. Lastly, findings were presented by explaining the formation of purchase intention in the egoistic and altruistic contexts. Practical implications were likewise discussed. Findings Findings manifest that citizens' purchase intention is influenced by egoistic and altruistic cognitions. An effective strategy is to show citizens the pro-environmental features of PH to promote its adoption because they value the environmental performance of housing. Meanwhile, consumers' social fitness also plays an essential role in decision-making, and the dual contradiction in the PH market is revealed. Originality/value This study extends the knowledge of psychological decision-making theories in the field of purchase intention toward PH by proposing an integrative framework of TPB and NAM. Results indicate a systematic and comprehensive understanding of consumers' decision-making in the PH domain. Moreover, results of this research contribute to specifying and refining the applicable contexts of TPB and NAM by adding two antecedents: subjective knowledge and environmental concern. This research contributes to the literature by being one of the first to investigate purchase intention toward a high-cost product with invisible technological innovation.
科研通智能强力驱动
Strongly Powered by AbleSci AI