{"title":"超越理论:从创业视角系统审视技术接受模式的优势和局限性","authors":"Ramy A. Rahimi, Grace S. Oh","doi":"10.1057/s41270-024-00318-x","DOIUrl":null,"url":null,"abstract":"<p>In today’s fiercely competitive business landscape, startups face numerous challenges in achieving scalability and sustainability. The integration of cutting-edge technologies such as Artificial Intelligence (AI), blockchain, and the Internet of Things (IoT) presents promising solutions to these challenges. However, understanding the intricacies of technology acceptance within the startup environment becomes paramount. Technology Acceptance Models (TAMs) have long served as foundational frameworks for understanding technology adoption and integration, but their effectiveness is hindered by inherent limitations. These limitations demand further exploration, particularly when viewed through the entrepreneurial lens. This paper offers a comprehensive analysis of the strengths and limitations inherent in TAM and its extensions, alongside other prominent technology acceptance models. By incorporating an entrepreneurial perspective, the analysis reveals additional challenges stemming from the dynamic nature of startup ecosystems. From a pragmatic standpoint, this paper provides actionable insights for technology-driven entrepreneurial organizations to navigate innovation and technology adoption decisions more intelligently. From a theoretical perspective, it contributes to the refinement and evolution of technology acceptance models, particularly in the context of entrepreneurial ventures. In light of these limitations, the paper offers strategic recommendations for future research endeavors. These include encouraging interdisciplinary collaboration, contextualizing models to suit startup dynamics, conducting longitudinal studies to capture evolving user perceptions, accounting for individual differences in technology adoption, and validating emerging models to reflect contemporary realities. Emphasis is placed on the entrepreneurial imperative of agility and adaptability in navigating the ever-changing landscape of technology acceptance. Moreover, the paper underscores the importance of a multidisciplinary approach and delineates practical implications for organizations and practitioners aiming to sustain technology acceptance and successful implementation within dynamic startup environments. By addressing these constraints, researchers can pave the way for the development of more robust and comprehensive models, better equipped to clarify and predict technology acceptance and usage patterns. Ultimately, this research underscores the critical need for ongoing refinement and innovation within the realm of technology acceptance, providing actionable insights to propel both scholarly discourse and entrepreneurial practice forward.</p>","PeriodicalId":43041,"journal":{"name":"Journal of Marketing Analytics","volume":"64 1","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Beyond theory: a systematic review of strengths and limitations in technology acceptance models through an entrepreneurial lens\",\"authors\":\"Ramy A. Rahimi, Grace S. Oh\",\"doi\":\"10.1057/s41270-024-00318-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In today’s fiercely competitive business landscape, startups face numerous challenges in achieving scalability and sustainability. The integration of cutting-edge technologies such as Artificial Intelligence (AI), blockchain, and the Internet of Things (IoT) presents promising solutions to these challenges. However, understanding the intricacies of technology acceptance within the startup environment becomes paramount. Technology Acceptance Models (TAMs) have long served as foundational frameworks for understanding technology adoption and integration, but their effectiveness is hindered by inherent limitations. These limitations demand further exploration, particularly when viewed through the entrepreneurial lens. This paper offers a comprehensive analysis of the strengths and limitations inherent in TAM and its extensions, alongside other prominent technology acceptance models. By incorporating an entrepreneurial perspective, the analysis reveals additional challenges stemming from the dynamic nature of startup ecosystems. From a pragmatic standpoint, this paper provides actionable insights for technology-driven entrepreneurial organizations to navigate innovation and technology adoption decisions more intelligently. From a theoretical perspective, it contributes to the refinement and evolution of technology acceptance models, particularly in the context of entrepreneurial ventures. In light of these limitations, the paper offers strategic recommendations for future research endeavors. These include encouraging interdisciplinary collaboration, contextualizing models to suit startup dynamics, conducting longitudinal studies to capture evolving user perceptions, accounting for individual differences in technology adoption, and validating emerging models to reflect contemporary realities. Emphasis is placed on the entrepreneurial imperative of agility and adaptability in navigating the ever-changing landscape of technology acceptance. 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引用次数: 0
摘要
在当今竞争激烈的商业环境中,初创企业在实现可扩展性和可持续性方面面临着诸多挑战。人工智能(AI)、区块链和物联网(IoT)等尖端技术的整合为这些挑战提供了前景广阔的解决方案。然而,了解初创企业环境中技术接受的复杂性变得至关重要。长期以来,技术接受模型(TAMs)一直是了解技术采用和集成的基础框架,但其有效性因固有的局限性而受到阻碍。这些局限性需要进一步探讨,尤其是通过创业视角来看待。本文全面分析了 TAM 及其扩展模型以及其他著名技术接受模型的优势和局限性。通过纳入创业视角,分析揭示了初创企业生态系统的动态性质所带来的额外挑战。从务实的角度来看,本文为技术驱动型创业组织提供了可行的见解,使其能够更明智地做出创新和技术采用决策。从理论角度来看,本文有助于完善和发展技术接受模型,尤其是在创业企业的背景下。鉴于这些局限性,本文为未来的研究工作提出了战略性建议。这些建议包括鼓励跨学科合作、根据初创企业动态调整模型、开展纵向研究以捕捉不断变化的用户感知、考虑技术采用中的个体差异,以及验证新兴模型以反映当代现实。本文强调了企业在不断变化的技术接受环境中必须具备的敏捷性和适应性。此外,论文还强调了采用多学科方法的重要性,并为旨在在动态的创业环境中保持技术接受度和成功实施的组织和从业人员阐述了实际意义。通过解决这些制约因素,研究人员可以为开发更强大、更全面的模型铺平道路,从而更好地阐明和预测技术接受和使用模式。最终,这项研究强调了在技术接受度领域不断完善和创新的迫切需要,为推动学术讨论和创业实践向前发展提供了可行的见解。
Beyond theory: a systematic review of strengths and limitations in technology acceptance models through an entrepreneurial lens
In today’s fiercely competitive business landscape, startups face numerous challenges in achieving scalability and sustainability. The integration of cutting-edge technologies such as Artificial Intelligence (AI), blockchain, and the Internet of Things (IoT) presents promising solutions to these challenges. However, understanding the intricacies of technology acceptance within the startup environment becomes paramount. Technology Acceptance Models (TAMs) have long served as foundational frameworks for understanding technology adoption and integration, but their effectiveness is hindered by inherent limitations. These limitations demand further exploration, particularly when viewed through the entrepreneurial lens. This paper offers a comprehensive analysis of the strengths and limitations inherent in TAM and its extensions, alongside other prominent technology acceptance models. By incorporating an entrepreneurial perspective, the analysis reveals additional challenges stemming from the dynamic nature of startup ecosystems. From a pragmatic standpoint, this paper provides actionable insights for technology-driven entrepreneurial organizations to navigate innovation and technology adoption decisions more intelligently. From a theoretical perspective, it contributes to the refinement and evolution of technology acceptance models, particularly in the context of entrepreneurial ventures. In light of these limitations, the paper offers strategic recommendations for future research endeavors. These include encouraging interdisciplinary collaboration, contextualizing models to suit startup dynamics, conducting longitudinal studies to capture evolving user perceptions, accounting for individual differences in technology adoption, and validating emerging models to reflect contemporary realities. Emphasis is placed on the entrepreneurial imperative of agility and adaptability in navigating the ever-changing landscape of technology acceptance. Moreover, the paper underscores the importance of a multidisciplinary approach and delineates practical implications for organizations and practitioners aiming to sustain technology acceptance and successful implementation within dynamic startup environments. By addressing these constraints, researchers can pave the way for the development of more robust and comprehensive models, better equipped to clarify and predict technology acceptance and usage patterns. Ultimately, this research underscores the critical need for ongoing refinement and innovation within the realm of technology acceptance, providing actionable insights to propel both scholarly discourse and entrepreneurial practice forward.
期刊介绍:
Data has become the new ore in today’s knowledge economy. However, merely storing and reporting are not enough to thrive in today’s increasingly competitive markets. What is called for is the ability to make sense of all these oceans of data, and to apply those insights to the way companies approach their markets, adjust to changing market conditions, and respond to new competitors.
Marketing analytics lies at the heart of this contemporary wave of data driven decision-making. Companies can no longer survive when they rely on gut instinct to make decisions. Strategic leverage of data is one of the few remaining sources of sustainable competitive advantage. New products can be copied faster than ever before. Staff are becoming less loyal as well as more mobile, and business centers themselves are moving across the globe in a world that is getting flatter and flatter.
The Journal of Marketing Analytics brings together applied research and practice papers in this blossoming field. A unique blend of applied academic research, combined with insights from commercial best practices makes the Journal of Marketing Analytics a perfect companion for academics and practitioners alike. Academics can stay in touch with the latest developments in this field. Marketing analytics professionals can read about the latest trends, and cutting edge academic research in this discipline.
The Journal of Marketing Analytics will feature applied research papers on topics like targeting, segmentation, big data, customer loyalty and lifecycle management, cross-selling, CRM, data quality management, multi-channel marketing, and marketing strategy.
The Journal of Marketing Analytics aims to combine the rigor of carefully controlled scientific research methods with applicability of real world case studies. Our double blind review process ensures that papers are selected on their content and merits alone, selecting the best possible papers in this field.