A PLS – SEM study to test the role of Social media in influencing Purchase Intention

I. Haque
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Abstract

In light of the growing importance of social networking marketing (SMM) to the profitability of tiny and medium-sized businesses (SMEs) and the relatively modest adoption rate of SMM among SMEs, this study seeks to determine which factors influence SMEs’ adoption of SMM. This study, unlike the majority of others, proposed a two-stage analysis combining the partial least squares (PLS) method with an artificial intelligence technique called an artificial neural network (ANN). Using a deep ANN architecture, the proposed model can make predictions with a 91% success rate. The marketing rate of social networking site adoption was found to be significantly affected by the strength of the relationship between perceived efficiency, user approval of use, perceived expenses, and encouragement from upper management, beneficial conditions, and vendor pressure. The findings of this study contribute to the expanding body of literature on online advertising by shedding light on the role played by technological, corporate, and ecological (TOE) variables in consumers’ adoption of social media promotional activities. Investment choices in digital marketing in comparable and non-competitive industries can benefit from the study’s findings, which can be used by policymakers as well as managers of SMM and consumer behavior.  
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透过PLS - SEM研究,检验社交媒体对购买意愿的影响
鉴于社交网络营销(SMM)对中小企业(SMEs)的盈利能力越来越重要,而中小企业对SMM的采用率相对较低,本研究旨在确定哪些因素影响中小企业对SMM的采用。与大多数其他研究不同,本研究提出了一种两阶段分析方法,将偏最小二乘(PLS)方法与一种称为人工神经网络(ANN)的人工智能技术相结合。使用深度人工神经网络架构,该模型可以以91%的成功率进行预测。研究发现,社交网站采用的营销率受到感知效率、用户使用认可、感知费用、高层管理鼓励、有利条件和供应商压力之间关系强度的显著影响。本研究的发现通过揭示技术、企业和生态(TOE)变量在消费者采用社交媒体促销活动中所起的作用,有助于扩大在线广告的文献主体。可比较和非竞争性行业的数字营销投资选择可以从研究结果中受益,这些研究结果可以为政策制定者以及SMM和消费者行为的管理者所用。
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