Skills for Business Enquiry Research Proposal
商业调查研究计划书的技能

Title of Journal:
期刊名称：

Entrepreneurial growth in digital business ecosystems: an integrated framework blending the knowledge‑based view of the firm and business ecosystems
数字商业生态系统中的创业增长：一个融合了公司和商业生态系统基于知识的观点的综合框架

Research Question:
研究问题：

Exploring How Small and Medium-Sized Enterprises (SMEs) in the Manufacturing Sector Can Leverage Generative AI for Enhanced Innovation in a Global Market
探索制造业的中小型企业 （SME） 如何利用生成式 AI 来增强全球市场的创新

Abstract
A小区

Entrepreneurial Growth in Digital Business Ecosystems: An Integrated Framework Blending the Knowledge-Based View of the Firm and Business Ecosystems [1] " provides a comprehensive study on how digital ecosystems impact entrepreneurial growth. Three ways in which small enterprises can improve their competitiveness are described: Internal Exploitation, Internal Exploration, and External Exploration. To respond to this argument, this paper replicates the research of it. Using a combination of qualitative and quantitative methods, it analyses how SMEs rely on digital tools to enhance their competitiveness in the market. This paper collects operational data, revenues, etc. of SMEs, as well as observing the mode of operation of these companies and analysing their intellectual capital. in order to assess the innovative role of digital technology.
数字商业生态系统中的创业增长：融合公司和商业生态系统基于知识的观点的综合框架 [1] “提供了关于数字生态系统如何影响创业增长的全面研究。描述了小企业提高竞争力的三种方式：内部开发、内部探索和外部探索。为了回应这一论点，本文复制了对它的研究。它采用定性和定量方法相结合的方法，分析了中小企业如何依靠数字工具来提高其市场竞争力。本文收集了中小企业的运营数据、收入等，并观察了这些公司的运营模式并分析了它们的智力资本。为了评估数字技术的创新作用。

I. Introduction
I. 引言

The digital business ecosystem, and generative AI in particular, is constantly shaping the contemporary technological landscape. Small and medium-sized enterprises (SMEs) in the manufacturing industry are constantly striving towards digitalisation in a highly competitive environment with big waves, but how should they do it? Where does success lie? Under the invisible pressure of many large enterprises, how should they go about finding their own space for survival? Compared with large enterprises, SMEs often lack sufficient resources and their intellectual capital (human capital, enterprise structure capital, customer capital) is relatively weak. But we have to admit that SMEs, especially in the manufacturing sector, have a crucial role to play in economic growth. Therefore, in the context of an increasingly digitised global economy, we are very curious about the relationship between the digital business ecosystem and these enterprises.We will discuss in this paper how small and medium-sized manufacturing industries can leverage generative AI to improve innovation and remain competitive in the global marketplace. In particular, what are the advantages of generative AI over traditional digital tools in terms of its strategic impact on internal knowledge reconstruction, external collaboration, and innovation processes?
数字商业生态系统，尤其是生成式 AI，正在不断塑造当代技术格局。制造业的中小企业 （SME） 在竞争激烈的环境中不断努力实现数字化，但他们应该如何做到这一点呢？成功在哪里？在众多大企业的无形压力下，他们该如何去寻找自己的生存空间呢？与大型企业相比，中小企业往往缺乏足够的资源，其智力资本（人力资本、企业结构资本、客户资本）相对薄弱。但我们不得不承认，中小企业，尤其是制造业的中小企业，在经济增长中发挥着至关重要的作用。因此，在全球经济日益数字化的背景下，我们非常好奇数字商业生态系统与这些企业之间的关系。在本文中，我们将讨论中小型制造业如何利用生成式 AI 来改善创新并在全球市场中保持竞争力。特别是，生成式 AI 在对内部知识重建、外部协作和创新过程的战略影响方面与传统数字工具相比有哪些优势？

The goal of this paper is to examine the three pathways of corporate entrepreneurial growth mentioned in Anlan Chen et al.'s article by replicating their study and giving our answer.
本文的目标是通过复制陈安澜等人的文章中提到的企业创业成长的三种途径，通过复制他们的研究并给出我们的答案。

II. Literature Review (approx. 700 words)
II. 文献综述（约 700 字）

Particularly in the industrial sector, where Generative AI is vital in promoting innovation and boosting competitiveness, it has evolved into a transforming technology revolutionising sectors. Generative AI, as described by Sedkaoui and Benaichouba (2024), is a powerful instrument for generating innovation and creativity in many fields. They draw attention to how this technology produces fresh designs, streamlines manufacturing techniques, and lets companies—including small and medium-sized firms (SMEs)—quickly develop and produce new products (Sedkaoui and Benaichouba, 2024).
特别是在工业领域，生成式人工智能在促进创新和提高竞争力方面至关重要，它已经发展成为一种变革性的技术，使行业发生革命性的变化。正如 Sedkaoui 和 Benaichouba （2024） 所描述的那样，生成式 AI 是在许多领域产生创新和创造力的强大工具。他们提请注意这项技术如何产生新的设计、简化制造技术，并让公司（包括中小型公司 （SME））快速开发和生产新产品（Sedkaoui 和 Benaichouba，2024 年）。

Generative AI helps many of the limitations usually experienced by smaller companies, such as limited resources and technical knowledge. Emphasising the importance of artificial intelligence in manufacturing, Peretz-Andersson et al. (2024) explain how small businesses may employ AI-driven solutions to improve resource orchestration, accelerate manufacturing, and more successfully mobilise qualified workers (Peretz-Andersson et al., 2024). By providing more flexibility, creativity, and speed in innovation, Generative AI enables SMEs to compete in a global market dominated by larger companies.
生成式 AI 有助于解决小公司通常遇到的许多限制，例如资源和技术知识有限。Peretz-Andersson 等人（2024 年）强调了人工智能在制造业中的重要性，解释了小企业如何利用人工智能驱动的解决方案来改善资源编排、加速制造并更成功地动员合格工人（Peretz-Andersson 等人，2024 年）。通过提供更高的灵活性、创造力和创新速度，生成式 AI 使中小企业能够在由大公司主导的全球市场中竞争。

Recent studies on digital transformation are prevalent, mostly with a focus towards how companies embrace new technology to increase creativity and competitiveness. According to Zaoui and Souissi (2020), digital transformation requires businesses to reinvent their structures to produce new value via AI and data integration, therefore going beyond mere technological adoption. Generative AI improves automation, customisation, and efficiency, which are vital for SMEs looking for fast and effective innovation (Zaoui and Souissi, 2020).
最近关于数字化转型的研究很普遍，主要集中在公司如何利用新技术来提高创造力和竞争力。根据 Zaoui 和 Souissi （2020） 的说法，数字化转型要求企业重塑其结构，以通过 AI 和数据集成产生新价值，因此不仅仅是技术采用。生成式 AI 提高了自动化、定制和效率，这对于寻求快速有效创新的中小企业至关重要（Zaoui 和 Souissi，2020 年）。

By allowing automation, customising, and rapid prototyping, generative artificial intelligence drives manufacturing innovation (Feuerriegel et al., 2024). By overcoming constraints including resources and knowledge, SMEs implementing artificial intelligence will be able to compete worldwide, where quick innovation is essential (Peretz-Andersson et al., 2024). In this way, AI supports SMEs in process optimisation and product design, improving time-to-market and competitiveness.
通过允许自动化、定制和快速原型制作，生成式人工智能推动了制造创新（Feuerriegel et al.， 2024）。通过克服资源和知识等限制，实施人工智能的中小企业将能够在全球范围内竞争，其中快速创新至关重要（Peretz-Andersson et al.， 2024）。通过这种方式，AI 在流程优化和产品设计方面为中小企业提供支持，从而缩短上市时间并提高竞争力。

Ayoko (2021) discusses how artificial intelligence is helping reshaping industries, therefore fostering adaptation and creativity. Adoption of artificial intelligence by SMEs can assist close the disparity with bigger companies by means of innovation despite limited means (Sedkaoui and Benaichouba, 2024).
Ayoko （2021） 讨论了人工智能如何帮助重塑行业，从而促进适应和创造力。尽管手段有限，但中小企业采用人工智能可以通过创新帮助缩小与大公司的差距（Sedkaoui 和 Benaichouba，2024 年）。

For manufacturing-based SMEs, generative AI presents both opportunities and difficulties. High expenses and a lack of technical knowledge are among the main obstacles that prevent SMEs from fully using AI technologies (Bhalerao et al., 2022). Such challenges can make it more difficult for them to compete with more established companies.
对于以制造业为基础的中小企业来说，生成式 AI 既是机遇也是困难。高昂的费用和缺乏技术知识是阻碍中小企业充分利用人工智能技术的主要障碍之一（Bhalerao et al.， 2022）。这些挑战可能使他们更难与更成熟的公司竞争。

While Schönberger (2023) emphasises the possible advantages of Generative AI—including automation, product customising, and enhanced efficiency—she also acknowledges the technical challenges SMEs have in expanding and combining AI solutions. Notwithstanding these challenges, artificial intelligence offers major chances for improving creativity (Schönberger, 2023)
虽然 Schönberger （2023） 强调了生成式 AI 的可能优势——包括自动化、产品定制和提高效率——但她也承认中小企业在扩展和组合 AI 解决方案方面面临的技术挑战。尽管存在这些挑战，但人工智能为提高创造力提供了重大机会（Schönberger，2023 年）

Generative machine learning, according to Sedkaoui and Benaichouba (2024), reduces time-to--market and gives SMEs a competitive edge by fostering creativity in product design. Generative artificial intelligence enables more rapid innovation and more customised products, therefore helping SMEs stand out in a market dominated by bigger companies (Sedkaoui and Benaichouba, 2024).
根据 Sedkaoui 和 Benaichouba （2024） 的说法，生成式机器学习通过培养产品设计的创造力来缩短上市时间并为中小企业提供竞争优势。生成式人工智能可实现更快速的创新和更多定制化的产品，从而帮助中小企业在由大公司主导的市场中脱颖而出（Sedkaoui 和 Benaichouba，2024 年）。

Zang (2018) vividly illustrates how SMEs have to achieve a proper mix between exploratory and exploitative innovation. Although bigger companies usually concentrate on improving their current procedures, SMEs are more likely to participate in exploratory innovation—that is, search for new prospects, develop ground-breaking ideas, and enter unexplored markets (Zang, 2018). Zang's insights into how structural holes affect innovation provide a useful framework for understanding the challenges SMEs face in competing with larger companies (Zang, 2018).
Zang （2018） 生动地说明了中小企业如何实现探索性创新和开发性创新的适当结合。尽管大公司通常专注于改进其当前程序，但中小企业更有可能参与探索性创新，即寻找新的前景，开发突破性的想法，并进入未开发的市场（Zang，2018）。Zang 对结构性漏洞如何影响创新的见解为理解中小企业在与大公司竞争时面临的挑战提供了一个有用的框架（Zang，2018 年）。

Schönberger (2023) shows how artificial intelligence, especially generative AI, helps SMEs to propel exploratory and exploitative innovation. Survival for SMEs usually depends on their capacity for radical innovation—that is, creation of unique goods or services to set them apart in cutthroat markets. Schönberger's study shows how Generative AI accelerates the innovation process so that SMEs may explore new frontiers while simultaneously enhancing current processes to sustain efficiency (Schönberger, 2023).
Schönberger （2023） 展示了人工智能，尤其是生成式 AI，如何帮助中小企业推动探索性和开发性创新。中小企业的生存通常取决于他们进行激进创新的能力，即创造独特的商品或服务，使其在竞争激烈的市场中脱颖而出。Schönberger 的研究展示了生成式 AI 如何加速创新过程，以便中小企业可以探索新的领域，同时增强当前流程以维持效率（Schönberger，2023 年）。

Emphasising the financial impact of Generative AI on SMEs, Soni (2023) lends still another aspect to this story. Using artificial intelligence to investigate new product lines and join specialised markets would help SMEs increase their client base and create more income (Soni, 2023). Generative artificial intelligence, according to Soni's research, is not only a tool for invention but also a catalyst for development enabling SMEs to investigate fresh prospects, increase income, and create a competitive edge in a market controlled by bigger companies (Soni, 2023).
Soni （2023） 强调了生成式人工智能对中小企业的财务影响，为这个故事提供了另一个方面。使用人工智能来调查新产品线并加入专业市场将有助于中小企业增加客户群并创造更多收入（Soni，2023 年）。根据 Soni 的研究，生成式人工智能不仅是一种发明工具，也是发展的催化剂，使中小企业能够调查新的前景、增加收入并在由大公司控制的市场中创造竞争优势（Soni，2023 年）。

References:
引用：

1.  Sedkaoui, S. and Benaichouba, R., 2024. Generative AI as a transformative force for innovation: a review of opportunities, applications and challenges. European Journal of Innovation Management.
1.Sedkaoui， S. 和 Benaichouba， R.，2024 年。 生成式 AI 作为创新的变革力量：机遇、应用和挑战回顾。 欧洲创新管理杂志。

2.  Peretz-Andersson, E., Tabares, S., Mikalef, P. and Parida, V., 2024. Artificial intelligence implementation in manufacturing SMEs: A resource orchestration approach. International Journal of Information Management, 77, p.102781.
2.Peretz-Andersson， E.、Tabares， S.、Mikalef， P. 和 Parida， V.，2024 年。制造业中小企业的人工智能实施：一种资源编排方法。国际信息管理杂志 （International Journal of Information Management），第 77 期，第 102781 页。

3.  Zaoui, F. and Souissi, N., 2020. Roadmap for digital transformation: A literature review. Procedia Computer Science, 175, pp.621-628.
3.Zaoui， F. 和 Souissi， N.，2020 年。数字化转型路线图：文献综述。Procedia 计算机科学 （Procedia Computer Science），第 175 期，第 621-628 页。

4.  Feuerriegel, S., Hartmann, J., Janiesch, C. and Zschech, P., 2024. Generative ai. Business & Information Systems Engineering, 66(1), pp.111-126.
4.Feuerriegel， S.、Hartmann， J.、Janiesch， C. 和 Zschech， P.，2024 年。生成式 AI。商业与信息系统工程，66（1），pp.111-126。

5.  Ayoko, O.B., 2021. Digital transformation, robotics, artificial intelligence, and innovation. Journal of Management & Organization, 27(5), pp.831-835.
5.Ayoko， O.B.，2021 年。数字化转型、机器人技术、人工智能和创新。管理与组织杂志，27（5），pp.831-835。

6.  Bhalerao, K., Kumar, A., Kumar, A. and Pujari, P., 2022. A study of barriers and benefits of artificial intelligence adoption in small and medium enterprise. Academy of Marketing Studies Journal, 26, pp.1-6.
6.Bhalerao， K.、Kumar， A.、Kumar， A. 和 Pujari， P.，2022 年。研究中小型企业采用人工智能的障碍和好处。营销研究学院杂志 （Academy of Marketing Studies Journal），第 26 期，第 1-6 页。

7.  Schönberger, M., 2023. Artificial Intelligence For Small And Medium-Sized Enterprises: Identifying Key Applications And Challenges. Journal of Business Management, 21, pp.89-112.
7.Schönberger， M.，2023 年。面向中小型企业的人工智能：确定关键应用和挑战。商业管理杂志 （Journal of Business Management），第 21 期，第 89-112 页。

8.  Zang, J., 2018. Structural holes, exploratory innovation and exploitative innovation. Management Decision, 56(8), pp.1682-1695.
8.Zang， J.，2018 年。结构性漏洞、探索性创新和开发性创新。管理决策，56（8），第 1682-1695 页。

9.  Soni, V., 2023. Impact of generative ai on small and medium enterprises' revenue growth: the moderating role of human, technological, and market factors. Reviews of Contemporary Business Analytics, 6(1), pp.133-153.
9.Soni， V.，2023 年。生成式 AI 对中小型企业收入增长的影响：人力、技术和市场因素的调节作用。当代商业分析评论，6（1），第 133-153 页。

IV. Research Design & Methodology (700–1000 words)
IV. 研究设计与方法论（700–1000 字）

Choice of research method
研究方法的选择

We chose as our research methodology the 2x2 matrix, a very common decision analysis tool that helps us to simplify complex problems. This research methodology is particularly useful when faced with the various influences in the digital business ecosystem, which can make it difficult to assess where they are pushing the organisation.
我们选择了 2x2 矩阵作为我们的研究方法，这是一种非常常见的决策分析工具，可以帮助我们简化复杂的问题。当面对数字商业生态系统中的各种影响时，这种研究方法特别有用，这可能使评估他们推动组织走向何方。

This approach will help us understand and categorise data through two key variables. It gives us a more intuitive understanding of how digital technology in turn helps SMEs to innovate and become more competitive. From there, we can further analyse the relationship between the various factors, thus preventing our research from getting bogged down in complex details.
这种方法将帮助我们通过两个关键变量来理解和分类数据。它让我们更直观地了解数字技术如何反过来帮助中小企业进行创新并提高竞争力。从那里，我们可以进一步分析各种因素之间的关系，从而防止我们的研究陷入复杂的细节。

The graphical presentation of the 2x2 matrix allows us to place the key factors and different phenomena, strategies and structures in the four quadrants, which allows us to place all the experimental samples in front of us at a glance, and to easily derive the optimal solutions and the maximum risk behaviours. Helps us to analyse the experimental subjects in depth.
2x2 矩阵的图形表示使我们能够将关键因素和不同的现象、策略和结构放在四个象限中，这使我们能够将所有实验样本一目了然地放在我们面前，并轻松得出最佳解决方案和最大风险行为。帮助我们深入分析实验对象。

The 2x2 matrix study, helped us to learn that XX is the highest priority in the entire digital business ecosystem and it plays a vital role in the entire matrix, based on which we assessed that Content Development, Internal Exploration and External Exploration are indeed the three important avenues of growth in the start-up phase of an SME. We also assessed what risks and opportunities these three avenues hold for SMEs, helping future SMEs in the manufacturing industry to combine complex technologies with market appeal, weighing the pros and cons and shaping a healthy digital economy model.
2x2 矩阵研究帮助我们了解到 XX 是整个数字商业生态系统中的最高优先级，它在整个矩阵中起着至关重要的作用，在此基础上，我们评估了内容开发、内部探索和外部探索确实是中小企业初创阶段的三大重要成长途径。我们还评估了这三种途径为中小企业带来的风险和机遇，帮助制造业的未来中小企业将复杂技术与市场吸引力相结合，权衡利弊，塑造健康的数字经济模式。

There are, of course, many other kinds of research methods that combine directional and quantitative approaches, but some are suitable for macro-level analyses rather than for delving into specific technologies (generative AI), and some are more suited to long-term strategic performance than to the capture of emerging technologies and innovation opportunities.
当然，还有许多其他类型的研究方法结合了定向和定量方法，但有些方法适合宏层面的分析，而不是深入研究特定技术（生成式人工智能），有些更适合长期战略绩效，而不是捕捉新兴技术和创新机会。

Matrix: Make or Buy?
Matrix：自制还是购买？

The following 2x2 matrix represents how organisations, Based on internal and external capacities, can integrate GenAI to enhance their operations:
以下 2x2 矩阵表示组织如何根据内部和外部能力集成 GenAI 以增强其运营：

	Internal Transformation 内部转型 Fully commit to using Generative AI, focusing on transforming internal processes and enhancing product development using in-house teams. 完全致力于使用生成式 AI，专注于转变内部流程和利用内部团队增强产品开发。	Collaborative Innovation 协同创新 Actively collaborate with ecosystem partners, like AI research institutions or tech firms, to leverage cutting-edge AI technology 积极与 AI 研究机构或科技公司等生态系统合作伙伴合作，以利用尖端的 AI 技术
		Incremental Innovation 渐进式创新 Focus on small-scale improvements by utilizing existing in-house knowledge and Generative AI for basic tasks 通过利用现有的内部知识和生成式 AI 来完成基本任务，专注于小规模的改进	Outsourced Exploration 外包勘探 Rely on external consultants or technology partners to experiment with Generative AI, acquiring external expertise and knowledge. 依靠外部顾问或技术合作伙伴来试验生成式 AI，获取外部专业知识和知识。
Focus 重点

Table: 2x2 Matrix analysing Make or Buy? Based on Adoption Level and Knowledge Base for Generative AI for innovation.
表：2x2 矩阵分析 Make or Buy？ 基于用于创新的生成式 AI 的采用水平和知识库。

Organisations exist in one of the four mentioned quadrants due to various internal and external factors like: Strategy, Resources or Market Position, etc. Depending on this, As the authors describe in their quote “The decision to make or buy is, thus, dependent upon three elements: how good a firm is currently at doing something, how good it is at learning specific capabilities, and the value of these capabilities as platforms into new markets.” (Kogut, B., & Zander, U. , 1992)
由于各种内部和外部因素，组织存在于上述四个象限之一，例如：战略、资源或市场地位等。根据这一点，正如作者在他们的引述中所描述的那样，“因此，做出或购买的决定取决于三个要素：一家公司目前在做某事方面的表现如何，它在学习特定能力方面的表现如何， 以及这些功能作为进入新市场的平台的价值。（Kogut， B.， & Zander， U. ， 1992年）

With reference to the 2x2 Matrix and the three scenarios in the quotes by Kogut and Zander (1992), let’s understand how organisations can innovate or adapt to gain competitive edge with integrating Generative AI tools. :
参考2x2 矩阵以及 Kogut 和 Zander （1992） 引述中的三个场景，让我们了解组织如何通过集成生成式 AI 工具进行创新或适应以获得竞争优势。 ：

Scenario 1: How good a firm is currently at doing something?
情景 1：一家公司目前在某件事上做得有多好？

This scenario highlights the organisation’s ability to integrate GenAI in their operations, existing capabilities and competencies. Organisations that possess a robust innovation culture, efficient internal processes and a strong R&D team, are best positioned in the internal transformation or incremental innovation quadrants. These organisations have the ability to utilise their resources to develop and implement new technologies in-house. (Zahra & George, 2002).
这种情况凸显了组织将 GenAI 集成到其运营、现有能力和能力中的能力。拥有强大的创新文化、高效的内部流程和强大的研发团队的组织，在内部转型或增量创新象限中处于最佳位置。这些组织有能力利用他们的资源在内部开发和实施新技术。（Zahra & George，2002年）。

On the other hand, the organisations that lack the ability to develop and manage AI projects inhouse should focus on forming outsourcing partnerships.(Quinn, 1999)
另一方面，缺乏内部开发和管理 AI 项目能力的组织应该专注于建立外包合作伙伴关系。（奎因，1999 年）

Scenario 2: How good is it at learning specific capabilities?
情景 2：学习特定能力的能力如何？

This scenario highlights the organisation’s ability to learn, train, adapt and integrate new GenAI technologies in their operations. The organisations that excel at forming alliances and partnerships with tech firms, research institutions and or consultants in order to learn and integrate new technologies, are best positioned in Collaborative Innovation
此场景凸显了组织在其运营中学习、培训、适应和集成新 GenAI 技术的能力。擅长与科技公司、研究机构和/或顾问建立联盟和伙伴关系以学习和整合新技术的组织，在协作创新方面处于最佳位置

and Outsourced Exploration quadrants. These organisations are open to joint development or they rely on external partnerships to gain the necessary expertise.
和外包勘探象限。这些组织对联合开发持开放态度，或者他们依靠外部合作伙伴关系来获得必要的专业知识。

On the other hand, the firms that lack the rigidity that is required to collaborate or outsource can work on building flexibility by implementing small and manageable AI projects or by forming low commitment partnerships. (Zaheer & Venkatraman, 1995).
另一方面，缺乏合作或外包所需的刚性的公司可以通过实施小型且可管理的 AI 项目或建立低承诺合作伙伴关系来建立灵活性。（Zaheer & Venkatraman，1995年）。

Scenario 3: Value of these capabilities as platforms into new markets?
情景 3：这些功能作为平台进入新市场的价值？

This scenario evaluates the potential value by integrating GenAI capabilities as means to enter into new markets or expand their current offerings.
此方案通过集成 GenAI 功能作为进入新市场或扩展其当前产品的手段来评估潜在价值。

The organisations whose AI implementation does not align with their business strategies, lack differentiation or innovation, or just the high cost outweigh the benefits. For example, implementing a generative AI tool for a purpose “A” while it was developed to solve a purpose “B”. AI may enhance their operations but not create new products, services, return on investment or enter markets aligned with the company’s objective. In such cases, GenAI implementation fails to generate the expected business value. These organisations can be found in any of the four quadrants.
AI 实施与其业务战略不一致、缺乏差异化或创新，或者只是高成本大于收益的组织。例如，为目的“A”实现生成式 AI 工具，而开发该工具是为了解决目的“B”而开发的。AI 可能会增强他们的运营，但不会创造新产品、服务、投资回报或进入与公司目标一致的市场。在这种情况下，GenAI 实施无法产生预期的业务价值。这些组织可以在四个象限中的任何一个象限中找到。

With reference to findings from the case study conducted by McKinsey along with the above hypothesis, lets evaluate how these organisations can benefit from implementing GenAI technologies:
参考麦肯锡进行的案例研究的结果以及上述假设，让我们评估这些组织如何从实施 GenAI 技术中受益：

Incremental Innovation: Companies should align AI technologies with core competencies and testing and scaling successful innovations incrementally. (Bresciani, S., & Eppler, M. J., 2010)
渐进式创新：公司应将 AI 技术与核心竞争力保持一致，并逐步测试和扩展成功的创新。 （Bresciani， S.， & Eppler， M. J.， 2010年）

Findings: “Generative AI could increase the productivity of the marketing function with a value between 5 and 15 percent of total marketing spending… Generative AI could significantly reduce the time required for ideation and content drafting, saving valuable time and effort.” (Chui, M. et al., 2023)
研究结果：“生成式 AI 可以提高营销职能的生产力，其价值在营销总支出的 5% 到 15% 之间......生成式 AI 可以显著减少构思和内容起草所需的时间，从而节省宝贵的时间和精力。（Chui， M. 等人，2023 年）

Internal Transformation: Ensure AI initiatives enhance product differentiation and align with long-term market expansion strategies.
内部转型：确保 AI 计划增强产品差异化并与长期市场扩张战略保持一致。

Findings: “Generative AI can substantially increase labour productivity across the economy… Generative AI could enable labour productivity growth of 0.1 to 0.6 percent annually through 2040, depending on the rate of technology adoption and redeployment of worker time into other activities” (Chui, M. et al., 2023)
研究结果：“生成式 AI 可以大幅提高整个经济的劳动生产率......到 2040 年，生成式人工智能可以使劳动生产率每年增长 0.1% 至 0.6%，具体取决于技术采用的速度和将工人时间重新部署到其他活动的速度“（Chui， M. et al.， 2023）

Outsourced Exploration: Companies should focus on market research and develop in-house capabilities while adopting a flexible partnership approach. (Felin, T., & Powell, T. C., 2016).
外包勘探：公司应专注于市场研究并发展内部能力，同时采用灵活的合作方式。（Felin， T.， & Powell， T. C.， 2016）。

Findings: “Pharma companies typically spend approximately 20 percent of revenues on R&D, and the development of a new drug takes an average of ten to 15 years… Generative AI’s ability to process massive amounts of data and model options can accelerate output across several use cases.” (Chui, M. et al., 2023)
调查结果：“制药公司通常将大约 20% 的收入用于研发，而新药的开发平均需要 10 到 15 年......生成式 AI 处理大量数据和模型选项的能力可以加快多个用例的输出。（Chui， M. 等人，2023 年）

Collaborative Innovation: Companies should work on deepening strategic partnerships to create GenAI solutions.
协作创新：公司应努力深化战略合作伙伴关系，以创建 GenAI 解决方案。

Findings: “Generative AI’s potential impact on the banking industry… relies on a large number of service representatives such as call-centre agents and wealth management financial advisers. Banks have started to grasp the potential of generative AI in their front lines and in their software activities. Early adopters are harnessing solutions such as ChatGPT as well as industry-specific solutions.” (Chui, M. et al., 2023)
调查结果：“生成式 AI 对银行业的潜在影响......依赖于大量的服务代表，例如呼叫中心代理和财富管理财务顾问。银行已经开始在其一线和软件活动中把握生成式 AI 的潜力。早期采用者正在利用 ChatGPT 等解决方案以及行业特定的解决方案。（Chui， M. 等人，2023 年）

References
引用

Chui, M. et al. (2023) The economic potential of Generative AI: The Next Productivity Frontier, McKinsey & Company. Available at: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#introduction (Accessed: 25 October 2024).
Chui， M. et al. （2023） 生成式人工智能的经济潜力：下一个生产力前沿，麦肯锡公司。见：https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#introduction（访问时间：2024 年 10 月 25 日）。

Quinn, J. B. (1999). Strategic outsourcing: Leveraging knowledge capabilities. Sloan Management Review, 40(4), 9-21.
奎因，JB（1999 年）。战略外包：利用知识能力。斯隆管理评论，40（4），9-21。

Zaheer, A., & Venkatraman, N. (1995). Relational governance as an interorganizational strategy: An empirical test of the role of trust in economic exchange. Strategic Management Journal, 16(5), 373-392.
Zaheer， A.， & Venkatraman， N. （1995）。关系治理作为一种组织间策略：信任在经济交换中的作用的实证检验。战略管理杂志，16（5），373-392。

Kogut, B., & Zander, U. (1992). Knowledge of the Firm, Combinative Capabilities, and the Replication of Technology. Organization Science, 3(3), 383-397.
Kogut， B.， & Zander， U. （1992）。了解公司、组合能力和技术复制。组织科学，3（3），383-397。

How AI brings renewed transparency and profitability to the property insurance industry (2022) ZestyAI. Available at: https://zesty.ai/news/how-ai-brings-renewed-transparency-and-profitability-to-the-property-insurance-industry (Accessed: 25 October 2024).
AI 如何为财产保险行业带来新的透明度和盈利能力 （2022） ZestyAI。可在：https://zesty.ai/news/how-ai-brings-renewed-transparency-and-profitability-to-the-property-insurance-industry（访问时间：2024 年 10 月 25 日）。

V. Expected Findings & Contributions (300–400 words)
V. 预期结果和贡献（300-400字）

Anticipated Results: Propose expected outcomes, such as demonstrating that SMEs using Generative AI can effectively engage in exploratory innovation, enabling them to compete with larger firms.
预期结果： 提出预期结果，例如证明使用生成式 AI 的中小企业可以有效地参与探索性创新，使他们能够与大公司竞争。

Contributions to Literature: Highlight how this study will contribute to literature on Generative AI and SME innovation, addressing gaps regarding SMEs in manufacturing.
对文献的贡献： 强调这项研究将如何为生成式 AI 和 SME 创新的文献做出贡献，解决制造业中 SME 的差距。

Practical Implications: Discuss potential implications for SME managers, such as how Generative AI can drive both incremental and radical innovation, and for policymakers on supporting digital infrastructure for SMEs.
实际意义： 讨论对中小企业管理者的潜在影响，例如生成式 AI 如何推动渐进式和激进式创新，以及对政策制定者支持中小企业数字基础设施的影响。

VI. Conclusion (200 words)
VI. 结论（200 字）

Summarise the importance of understanding how SMEs can leverage Generative AI to drive innovation and remain competitive in global markets.
总结了解中小企业如何利用生成式 AI 来推动创新并在全球市场中保持竞争力的重要性。

VII. Timeline (150 words)
VII. 时间表（150 字）

Include a Gantt chart or detailed timeline, specifying when different parts of the research (literature review, data collection, analysis) will be completed.
包括甘特图或详细的时间表，指定研究的不同部分（文献综述、数据收集、分析）的完成时间。

VII. Bibliography (no word limit)
VII. 参考书目（无字数限制）

Shared Responsibility:
责任共担：

Cite Ordóñez de Pablos’ paper extensively, alongside other academic and industry sources on Generative AI, exploratory innovation, and SMEs in manufacturing. Ensure all sources are properly referenced using Harvard referencing styles.
广泛引用 Ordóñez de Pablos 的论文，以及其他关于生成式 AI、探索性创新和制造业中小企业的学术和行业资源。确保使用Harvard 引用样式正确引用所有源。