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人工智能采用对企业竞争优势的影响研究
Research on the impact of AI adoption on the competitive advantages of enterprises
探究人工智能与实体经济的融合成效是重构产业发展范式与催生智能经济形态的重要议题。现有研究多从生产效率、就业结构等局部视角考察人工智能的微观影响,缺乏对企业能否利用人工智能提升竞争优势的整体性讨论。基于此,本文以2007—2022年沪深A股制造业上市公司为研究样本,运用固定效应模型实证检验人工智能采用对企业竞争优势的影响。研究发现,人工智能采用有助于提升企业竞争优势,在考虑了内生性问题与替换变量等稳健性检验后结论依然成立。情境因素分析表明,企业只有将人工智能根植于具体的应用场景、为其配置互补性资产以及强化自身技术能力才能切实提升竞争优势。机制检验显示,人工智能采用通过强化生产效率和创新效率来提升企业竞争优势。进一步研究表明,人工智能对竞争优势的提升作用在后发企业和采用速度较快的企业中更为明显。本文的研究结论不仅回应了作为通用技术的人工智能是否有助于提升企业竞争优势这一基本问题,同时为传统企业如何利用人工智能创造独特价值、提升竞争地位提供了经验证据和管理启示。
Exploring the integration of artificial intelligence (AI) with the real economy is an important issue for reconstructing industrial development paradigms and fostering the emergence of intelligent economic forms. Existing research primarily examines the micro-level impacts of AI from perspectives such as production efficiency and employment structure, but lacks a comprehensive discussion on whether enterprises can leverage AI to enhance their competitive advantage. Based on this, this study used manufacturing listed companies in the Shanghai and Shenzhen A-share markets from 2007 to 2022 as the research sample to empirically test the impact of AI adoption on enterprise competitive advantages using a fixed effects model. The results showed that AI adoption contributes to enhancing enterprise competitive advantage, and the conclusion remains robust even after addressing issues such as endogeneity, substitution variables, and other robustness tests. The contextual analysis indicated that enterprises can effectively improve their competitive advantage only when AI is embedded in specific application scenarios, complemented by appropriate assets, and supported by enhanced technological capabilities. The mechanism tests revealed that AI adoption enhances competitive advantage through improvements in production efficiency and innovation efficiency. Further research suggested that the positive impact of AI on competitive advantage is more evident in latecomer enterprises and those with faster adoption speeds. The conclusion of this study has not only addressed the fundamental question of whether AI, as a general-purpose technology, contributes to improving enterprise competitive advantage but also will provide certain empirical evidence and managerial insights on how traditional enterprises can leverage AI to create unique value and enhance their competitive position.
AI adoption / production efficiency / innovation efficiency / enterprise competitive advantage
| [1] |
|
| [2] |
In the future, artificial intelligence (AI) is likely to substantially change both marketing strategies and customer behaviors. Building from not only extant research but also extensive interactions with practice, the authors propose a multidimensional framework for understanding the impact of AI involving intelligence levels, task types, and whether AI is embedded in a robot. Prior research typically addresses a subset of these dimensions; this paper integrates all three into a single framework. Next, the authors propose a research agenda that addresses not only how marketing strategies and customer behaviors will change in the future, but also highlights important policy questions relating to privacy, bias and ethics. Finally, the authors suggest AI will be more effective if it augments (rather than replaces) human managers.
|
| [3] |
|
| [4] |
孟凡生, 赵艳, 冯耀辉, 等. 人工智能专利网络对企业智能化发展的影响[J]. 科研管理, 2024, 45(7):118-126.
|
| [5] |
Artificial Intelligence is poised to transform all facets of marketing. In this study, we examine the link between firms’ focus on AI in their 10-K reports and their gross and net operating efficiency. 10-K reports are a salient source of insight into an array of issues in accounting and finance research, yet remain relatively overlooked in marketing. Drawing upon economic and marketing theory, we develop a guiding framework to show how firms’ AI focus could be related to gross and net operating efficiency. We then use a system of simultaneous equations to empirically test the relationship between AI focus and operating efficiency. Our findings confirm that US-listed firms are in a state of impending transformation with regards to AI. We show how AI focus is associated with improvements in net profitability, net operating efficiency and return on marketing-related investment while reducing adspend and creating jobs.
|
| [6] |
|
| [7] |
|
| [8] |
|
| [9] |
Across many fields of social science, machine learning (ML) algorithms are rapidly advancing research as tools to support traditional hypothesis testing research (e.g., through data reduction and automation of data coding or for improving matching on observable features of a phenomenon or constructing instrumental variables). In this paper, we argue that researchers are yet to recognize the value of ML techniques for theory building from data. This may be in part because of scholars’ inherent distaste for predictions without explanations that ML algorithms are known to produce. However, precisely because of this property, we argue that ML techniques can be very useful in theory construction during a key step of inductive theorizing—pattern detection. ML can facilitate algorithm supported induction, yielding conclusions about patterns in data that are likely to be robustly replicable by other analysts and in other samples from the same population. These patterns can then be used as inputs to abductive reasoning for building or developing theories that explain them. We propose that algorithm-supported induction is valuable for researchers interested in using quantitative data to both develop and test theories in a transparent and reproducible manner, and we illustrate our arguments using simulations.
|
| [10] |
|
| [11] |
|
| [12] |
|
| [13] |
In the wake of media hype about artificial intelligence (AI)/human collaboration, organizations are investing considerable resources into developing and using AI. In this paper, we draw on theories of technology in organizations to frame new directions for the study of what it means to work “with” AI. Drawing on prior literature, we consider how interactions between users and AI might unfold through theoretical lenses which cast technology as a tool and as a medium. Reflecting on how AI technologies diverge from technologies studied in the past, we propose a new perspective, which considers technology as a counterpart in a system of work that includes its design, implementation, and use. This perspective encourages developing a grounded understanding of how AI intersects with work, and therefore ethnography, building on thick descriptions, is an apt approach. We argue that relational ethnographic approaches can assist organization theorists in navigating the methodological challenges of taking a counterpart perspective and propose several strategies for future research.
|
| [14] |
|
| [15] |
|
| [16] |
This article explores the role of artificial intelligence (AI) in aiding personalized engagement marketing—an approach to create, communicate, and deliver personalized offerings to customers. It proposes that consumers are ready for a new journey in which AI is a tool for endless options and information that are narrowed and curated in a personalized way. It also provides predictions for managers regarding the AI-driven environment on branding and customer management practices in both developed and developing countries.
|
| [17] |
The era of big data and analytics is upon us and is changing the world dramatically. The field of Information Systems should be at the forefront of understanding and interpreting the impact of both technologies and management so as to lead the efforts of business research in the big data era. We need to prepare ourselves and our students for this changing world of business. In this discussion, we focus on exploring the technical and managerial issues of business transformation resulting from the insightful adoption and innovative applications of data sciences in business. We end by providing an overview of the papers included in this special issue and outline future research directions.
|
| [18] |
We gather detailed data on organizational practices and information technology (IT) use at 253 firms to examine the hypothesis that external focus—the ability of a firm to detect and therefore respond to changes in its external operating environment—increases returns to IT, especially when combined with decentralized decision making. First, using survey-based measures, we find that external focus is correlated with both organizational decentralization, and IT investment. Second, we find that a cluster of practices including external focus, decentralization, and IT is associated with improved product innovation capabilities. Third, we develop and test a three-way complementarities model that indicates that the combination of external focus, decentralization, and IT is associated with significantly higher productivity in our sample. We also introduce a new set of instrumental variables representing barriers to IT-related organizational change and find that our results are robust when we account for the potential endogeneity of organizational investments. Our results may help explain why firms that operate in information-rich environments such as high-technology clusters or areas with high worker mobility have experienced especially high returns to IT investment and suggest a set of practices that some managers may be able to use to increase their returns from IT investments.
|
| [19] |
|
| [20] |
|
| [21] |
董直庆, 姜昊, 王林辉. “头部化”抑或“均等化”:人工智能技术会改变企业规模分布吗?[J]. 数量经济技术经济研究, 2023, 40(2):113-135.
|
| [22] |
|
| [23] |
|
| [24] |
蔡贵龙, 邓景, 葛锐, 等. 客户行业竞争地位与供应商企业绩效[J]. 会计研究, 2022(11):72-86.
|
| [25] |
王永钦, 董雯. 机器人的兴起如何影响中国劳动力市场? 来自制造业上市公司的证据[J]. 经济研究, 2020, 55(10):159-175.
|
| [26] |
何小钢, 朱国悦, 冯大威. 工业机器人应用与劳动收入份额:来自中国工业企业的证据[J]. 中国工业经济, 2023(4):98-116.
|
| [27] |
姚加权, 张锟澎, 郭李鹏, 等. 人工智能如何提升企业生产效率? 基于劳动力技能结构调整的视角[J]. 管理世界, 2024, 40(02):101-116.
|
| [28] |
|
| [29] |
黄群慧, 余泳泽, 张松林. 互联网发展与制造业生产率提升:内在机制与中国经验[J]. 中国工业经济, 2019(8):5-23.
|
| [30] |
尹志锋, 曹爱家, 郭家宝, 等. 基于专利数据的人工智能就业效应研究:来自中关村企业的微观证据[J]. 中国工业经济, 2023(5):137-154.
|
| [31] |
|
| [32] |
|
| [33] |
|
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