数智赋能区域传统工业企业创新路径研究——基于TOEP理论框架

数智化是产业升级发展方向,其要素配置方式是赋能传统工业企业创新的关键。以“技术—组织—环境—进程”框架构建影响因素维度,运用模糊集定性比较分析方法(fsQCA)对中国内地31个省级行政区数据进行组态分析,探究数智赋能传统工业企业创新路径。结果发现：前因变量中不存在必要条件,数智赋能传统工业企业创新存在5条典型组态路径,其中“技术—组织”侧重技术研发推动,“环境—进程”由需求拉动引发,两者形成互补性赋能模式;进程维度在4条赋能路径中作为有效条件,与以往忽视进程维度不同,数智应用对传统工业企业创新发挥关键作用;高创新绩效区域均选择“技术—组织—环境-进程”路径,其它区域可依据自身资源条件选择与之相匹配的路径,通过增强要素间协同能力优化赋能效果。     

数智赋能对制造业服务化转型的作用机制研究——基于有调节的中介效应分析

数智赋能推动传统制造业服务化转型在学术界和企业界广受重视。基于“数智赋能—业务流程敏捷性—服务化转型绩效”的逻辑框架,探讨数智赋能在制造业服务化转型中的关键作用机制。根据219家我国传统制造企业的有效问卷调查,实证分析传统制造业通过数智赋能提升服务化转型绩效的作用路径。研究发现,数智赋能通过业务流程敏捷性的中介作用提升传统制造企业的竞争力和适应性,从而对服务化转型绩效产生正向影响;双元组织学习正向调节了业务流程敏捷性和服务化转型绩效之间的关系。     

数智融合赋能与制造业企业绿色转型

数智融合作为推进制造企业绿色转型升级的关键路径,对发展数字中国与制造强国具有重要作用。以2007—2021年中国A股制造业上市公司为研究对象,综合运用文本分析法、耦合协调度模型、门槛效应模型等,实证检验了数智融合对制造业企业绿色转型的影响与作用机制。结果表明：数智融合对制造业企业绿色转型具有显著赋能作用,并且在经过了一系列稳健性检验后结论依然成立;数智融合通过绿色认知和绿色创新赋能制造业企业绿色转型;数智融合对企业绿色转型的促进作用在国有企业、重污染企业中更大;数智融合对企业绿色转型的影响呈现非线性特征,存在门槛效应。因此,为充分发挥数智融合对制造业企业绿色转型的赋能作用,应运用大数据、人工智能、物联网、云计算等技术改善企业管理;强化企业绿色认知,推动企业绿色创新,构建数智融合赋能绿色转型新模式;应发挥国有企业的示范效应,继续深入实施数字经济发展战略,积极推动“双碳”目标的实现,以“数字化、智能化”为核心,推进企业绿色转型。     

基于数智化转型赋能的云会计应用及风险应对

数智化是当前社会企业转型赋能的方向与路径。文章基于数智化转型赋能的背景聚焦会计信息化的全新发展阶段——云会计，探讨了云会 计的优势、现状与风险，并提出了相关建议。     

数字人才与中小企业开放式创新绩效：一个中介调节效应模型

随着数字经济的深入发展,人才在数字化转型过程中的作用得到了学术界和企业界的大量关注。在数字化转型背景下,数字人才如何影响开放式创新缺乏深入探讨。立足数字化赋能的视角,基于“数字人才—知识搜索—开放式创新”的基本逻辑,构建了一个有中介的调节效应模型。在此基础上,利用1930家中小企业的调研数据,对提出的理论模型进行实证检验。研究发现：数字人才有助于改善中小企业开放式创新绩效;数字人才通过知识搜索广度和知识搜索深度正向影响中小企业开放式创新绩效,知识搜索广度和知识搜索深度对中小企业开放式创新绩效也有正向影响。同时,IT能力正向调节了数字人才通过知识搜索广度和知识搜索深度对中小企业开放式创新绩效关系。研究结论对中小企业如何提升开放式创新绩效以及如何应对数字化转型都具有重要指导意义。     

数智赋能驱动场景价值创造实现机理—基于海尔智家和小米的案例分析

采用探索性案例研究方法,选取海尔智家和小米作为研究对象,探究了数智赋能驱动场景价值创造的实现机理。研究发现：场景价值创造经历场景价值辨识、场景价值聚合与场景价值共生三个环节。在每一环节中,企业需要通过借力相应的数智赋能作用,开展供需资源行动,培育与之适应的场景价值创造能力。具体而言,企业基于数智分析作用,促进客户需求资源识别,发展内容感知能力,实现场景价值辨识;基于数智连接作用,促进供给资源拼凑,发展触点适配能力,实现场景价值聚合;基于数智交互作用,促进供需资源匹配,发展敏捷响应能力,实现场景价值共生。本研究扩展了现有数智赋能和场景价值创造相关研究,对指导企业在数智背景下塑造场景价值优势具有启示意义。     

企业开放式创新社区网络创新绩效内部影响因素分析——NK模型视角

开放式创新能广泛吸收企业内外部创新资源,加速企业技术创新进程,提升企业核心竞争力。在深入研究开放式创新理论,并对国内外开放式创新现状和问题进行广泛调研的基础上,揭示影响企业开放式创新社区网络创新绩效的内部因素,通过问卷调查,运用适合度景观理论和NK模型,对各因素的相互作用及其对开放式创新社区网络创新绩效的影响进行实证研究。结果显示,在一定的内部条件下,内部影响因素的有效组合配置,能使企业获得较好的开放式创新绩效,为企业进一步有效实施开放式创新奠定基础。     

环境动态性、资源拼凑与中小企业创新

本文以国内278家中小企业数据为研究样本,运用OLS模型实证分析了环境动态性对中小企业创新的影响以及资源拼凑的中介效用.结果表明,环境动态性能够提升中小企业创新绩效.进一步地,笔者将中小企业创新分为渐近式创新和突破式创新,将资源拼凑分为计划拼凑和即兴拼凑,采用中介效应模型研究环境动态性影响渐进式创新和突破式创新的渠道.从影响机制来看,环境动态性能够促进企业的计划拼凑和即兴拼凑行为,进而有效提升渐进式创新绩效和突破式创新绩效.本文的结论不仅丰富了资源拼凑和创新理论的研究内容,也为中小企业解决动态环境和资源匮乏带来的创新难困境提供了有效路径.     

数智赋能新型工业化发展

<正>数字化和智能化在推动新型工业化发展的进程中发挥着至关重要的作用。数智赋能新型工业化具有重要意义和鲜明特征。要采取适当举措积极落实好关于推动新型工业化的重要部署,助力新型工业化重要成果高效落地。一、数智赋能新型工业化的重要意义数智赋能是推动新型工业化的主要路径和关键抓手。数智赋能新型工业化具有三方面重要意义。第一,数智赋能新型工业化是产业技术再造必须夯实的底座。改革开放四十多年来,     

企业开放式创新社区网络创新绩效内部影响因素分析

开放式创新能广泛吸收企业内外部创新资源,加速企业技术创新进程,提升企业核心竞争力。在深入研究开放式创新理论,并对国内外开放式创新现状和问题进行广泛调研的基础上,揭示影响企业开放式创新社区网络创新绩效的内部因素,通过问卷调查,运用适合度景观理论和NK模型,对各因素的相互作用及其对开放式创新社区网络创新〖JP〗绩效的影响进行实证研究。结果显示,在一定的内部条件下,内部影响因素的有效组合配置,能使企业获得较好的开放式创新绩效,为企业进一步有效实施开放式创新奠定基础。     

Different approaches of pattern management and strategic intelligence

The world around us contains endless amounts of information. That information is mostly loose in our minds. Very often, it does not at first contact fit in with our conventional understanding, experience or any context we are used to. Hence, we may say that we are overwhelmed with constantly changing raw data, and strategic actors especially tend to be short of more rapid, up-to-date, valid and in-depth understanding of the transforming business landscape and social environment. Strategic intelligence is an emerging field of business consulting, which aims to undertake the task of revealing large, complex or complicated issues of transformation in a more understandable form. Pattern management, however, can be seen as one field or one approach of strategic intelligence. It is an approach that may, on one hand, be based more on empiric data and formal structures than other forms of strategic intelligence, but, on the other hand, it is a very heuristic approach to integrate quantitative data, reasoning and narratives. The main attempts of this article are, first, to show, what are in general the most commonly used ways of managing, finding, drawing, reasoning or anticipating patterns from our environment, and second, to locate how the concept of pattern can be understood in different ways. From the gathered knowledge, this article presents three main categories of reasoning patterns: empirical calculation (EC) is common especially in enterprise consulting. Theory proving with observations (TPO) is common especially in natural sciences, and real combining (RC) is common especially in qualitative research and in narrative.     

The effects of intelligence on price discovery and market efficiency

The influence of speculation on market performance has long been discussed. Under the framework of bounded rationality in which traders are endowed with different intelligence levels in terms of different learning styles or different representations of intelligence, we examine the effects of traders’ intelligence on price discovery based on “intraday” data, and market efficiency. We find that intelligence does help improve market performance. However, the influence of different intelligence levels on the market crucially depends on the characteristics of learning styles or the representation of intelligence.     

The impact of artificial intelligence on economic growth and welfare

Focusing on the self-accumulation ability and the nonrival characteristic of artificial intelligence (AI), this paper develops a three-sector endogenous growth model and investigates the impact of the development of AI along the transitional dynamics path and the balanced growth path. The development of AI can increase economic growth along the transitional dynamics path, and can increase household short-run utility if an increase in the accumulation of AI is due to the rising productivity in the goods or AI sector, but can be detrimental to household short-run utility if an increase in the accumulation of AI is because firms use more AI to replace human labor. In addition, the development of AI is not necessarily beneficial to household welfare in the long run. The main results are unaffected when considering the case where AI can improve the accumulation of human capital, the traditional research and development model, and different kinds of physical capital.     

Linking technology intelligence to open innovation

The explosive growth of the Internet has led to a dramatic increase in data sources for (competitive) technology intelligence. Appropriate implementation and use of IT tools to gather and analyze these data is of key importance for the creation of actionable technology intelligence. A strategy to optimize investments in the identified technologies becomes of paramount importance if an organization wants to match knowledge and ideas originating from outside of the organization with internal core competences. Such a strategy can create competitive advantage by effectively linking technology intelligence to open innovation.We show how VIB, a life sciences research organization, has established technology intelligence processes to identify a multitude of external technologies of interest, which are subsequently “probed” for their potential and fit with VIB using real options reasoning, thereby supporting open innovation. Our methodology may be useful for other organizations which are considering implementing open innovation approaches.     

Technology commercialization intelligence: Organizational antecedents and performance consequences

External technology commercialization, e.g., by means of technology licensing, has recently gained in importance. Despite imperfections in technology markets, out-licensing constitutes a major technology commercialization channel. Although the identification of licensing opportunities represents a significant managerial challenge, prior research has relatively neglected these activities. Therefore, we develop the concept of ‘technology commercialization intelligence’ (TCI), which refers to the observation of a firm's environment with particular focus on identifying technology licensing opportunities. Grounded in a dynamic capabilities perspective, we test five hypotheses regarding organizational antecedents and performance consequences of TCI, drawing on data from a survey of 152 companies. The empirical findings provide strong support for the importance of the TCI concept. The findings deepen our understanding of the discrepancies between successful pioneering firms active in technology licensing and many others being less successful. The results have major implications for technology exploitation in open innovation processes.     

QTIP: Quick technology intelligence processes

Empirical technology analyses need not take months; they can be done in minutes. One can thereby take advantage of wide availability of rich science and technology publication and patent abstract databases to better inform technology management. To do so requires developing templates of innovation indicators to answer standard questions. Then, one can automate routines to generate composite information representations (“one-pagers”) that address the issues at hand, the way that the target users want.     

数字经济产业园区企业网络嵌入、数字化能力与开放式创新

以社会网络理论、动态能力理论等为理论基础,研究数字经济产业园区企业网络嵌入对开放式创新的影响。研究发现：网络嵌入显著正向影响数字经济产业园区企业开放式创新。机制分析表明,网络嵌入通过影响数字化能力进而提升数字经济产业园区企业开放式创新水平。进一步讨论发现,网络嵌入的子维度关系性网络嵌入对数字化能力的三个子维度数字感知能力、数字化运营能力、数字资源协同能力具有显著的正向影响;网络嵌入的子维度结构性网络嵌入对数字化能力的子维度数字感知能力和数字化运营能力具有显著的正向影响,但对数字资源整合能力的影响并不显著。本文对于数字经济产业园区构建协同共生网络生态,提高园区企业网络嵌入程度,进而培育和构建数字化能力,提高区域开放式创新水平提供了理论基础和一定的实践指导。     

The role of scanning in open intelligence systems

Every month, SRI Consulting Business Intelligence (SRIC-BI) professionals assemble more than 100 short abstracts of developments that they perceive to be signals of change, discontinuities, inflection points, outliers, or disruptive developments. The effort is part of a continuous scanning process and Scan program that allows SRIC-BI to gauge the ongoing turbulent confluence of culture, commerce, and technology that defines today's business environment. For more than 25 years, scanning has played an essential role in SRIC-BI's and SRI International's foresight capabilities by providing a systematic means for surveying the broad external environment for change vectors. Traditional monitoring processes in most organizations are largely arbitrary, depending on what concerned individuals or leaders in the organization are reading, thinking about, and sharing informally with each other. But in today's world, arbitrary is insufficient. No foresight function can operate with confidence without a disciplined process for spotting new patterns of change and bringing those issues into the organization for early consideration and action. This article describes the scanning process as SRIC-BI practices it, the importance of open intelligence systems, what benefits the scanning process can provide to organizations, and what problems organizations typically run into when setting up scanning systems.     

Don’t let your robots grow up to be traders: Artificial intelligence, human intelligence, and asset-market bubbles

Researchers who have examined markets populated by “robot traders” have claimed that the high level of allocative efficiency observed in experimental markets is driven largely by the “intelligence” implicit in the rules of the market. Furthermore, they view the ability of agents (artificial or human) to process information and make rational decisions as unnecessary for the efficient operation of markets. This paper presents a new series of market experiments that show that markets populated with standard robot traders are no longer efficient if time is a meaningful element, as it is in all asset markets. While simple two-season markets with human subjects reliably converge to an efficient equilibrium, markets with minimally intelligent robot traders fail to attain this equilibrium. Instead, these markets overshoot the equilibrium and then crash below it. In addition to firmly establishing the role of trader intelligence in asset-market equilibrium, these experiments also provide insights into why bubbles and crashes are consistently observed in many asset-market laboratory experiments using human subjects.     

The relation between income,intelligence, education and social background

In this article the authors have tried to estimate the relative importance of education, intelligence and social background for the explanation of the variations in personal income. The investigation has been carried out on Swedish data which were generously made available to them by Professor Husén of Stockholm University.The results pointed to education as the most important factor in explaining the income variations, followed by the social class of the fathers and the level of intelligence in this order. The relations turned out to be strongly curvilinear.Moreover, the interactions between the explanatory variables have been studied. It was found that their combined contribution is much larger than would correspond to additivity, in particular for the higher classes of the variables. They clearly reinforce each other.