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Artificial intelligence and industrial innovation: Evidence from German firm-level data
Abstract
This paper analyses the link between the use of Artificial Intelligence (AI) and innovation performance in firms. Based on firm-level data from the German part of the Community Innovation Survey (CIS) 2018, we examine the role of different AI methods and application areas in innovation. The results show that 5.8% of firms in Germany were actively using AI in their business operations or products and services in 2019. We find that the use of AI is associated with annual sales with world-first product innovations in these firms of about €16 billion (i.e. 18% of total annual sales of world-first innovations). In addition, AI technologies have been used in process innovation that contributed to about 6% of total annual cost savings of the German business sector. Firms that apply AI broadly (using different methods for different applications areas) and that have already several years of experience in using AI obtain significantly higher innovation results. These positive findings on the role of AI for innovation have to be interpreted with caution as they refer to a specific country (Germany) in a situation where AI started to diffuse rapidly.
... Chen and Tajdini (2024) also indicate that the high technological turbulent environment provides more opportunities for firms to capitalise on AI technologies. In an environment with abundant opportunities, moreover, firms that pay selective attention to AI could generate higher sales volumes and better performance (Rammer et al., 2022). Therefore, we establish the following hypothesis: ...
... For example, Hagedoorn and Cloodt (2003) indicate that the number and citations of patents are correlated with the performance of new product development. Recently, scholars have found that the patents of AI-driven technologies significantly contribute to companies launching world-first products and innovations (Igna and Venturini, 2023;Rammer et al., 2022). In addition, patenting provides a firm with the ready-to-use technological knowledge complementary to other specialised commercial assets to enhance productivity (Colombo et al., 2006) and increase efficiency (Lichtenthaler, 2019), particularly when AI patenting can be either a core production method or a complementary way to existing applications (Yang, 2022). ...
... For one thing, AI patenting follows a focal firm's attention to the specific issue regarding the development of AI technologies and deploys the firm's resources into that domain to create value (Haenlein and Kaplan, 2019). For another thing, AI patenting captures value for the focal firm through the increase in innovation (Igna and Venturini, 2023;Rammer et al., 2022), productivity (Benassi et al., 2022;Yang, 2022), growth (Li et al., 2023), ...
Drawing upon the theory of attention-based view (ABV), we explore why and how a firm’s attention affects its action on artificial intelligence (AI) and performance. As attention is a cognitive mechanism for information processing, we argue that an increase in a firm’s selective attention to AI enhances the firm’s performance, particularly when the focal firm is facing an environment with abundant opportunities. In addition, an increase in the focal firm’s attention to AI drives its action on AI patenting, which is one major strategy for responding to new technologies and enhancing firm performance. Moreover, we further assert that the positive impact of a firm’s attention to AI on firm performance is mediated by the action on AI patenting. Testing 300 companies indexed by the S&P 500 from 2012 to 2022, we find our premises supported and contribute to the current research on AI, ABV, and innovation management.
... For instance, AI could boost the productivity of labor by automating specified work and augmenting human labor [5]. AI is likely to also lead to the formation of business of new types that will contribute to extra sales and build up productivity as a consequence of growth of output [6]. ...
... The paper of Artificial intelligence and firm-level productivity [6] addressed this issue fully by utilizing data from a representative, large-scale survey [11] that is full of information on firms' adoption of AI technology and investigating the impact of AI on the productivity at firm-levels. Its author analyzed both panel data and cross-section from the German part of Community Innovation Survey (CIS) of the European Commission's [6]. ...
... The paper of Artificial intelligence and firm-level productivity [6] addressed this issue fully by utilizing data from a representative, large-scale survey [11] that is full of information on firms' adoption of AI technology and investigating the impact of AI on the productivity at firm-levels. Its author analyzed both panel data and cross-section from the German part of Community Innovation Survey (CIS) of the European Commission's [6]. The German segment of the European Commission's Community Innovation Survey (CIS) included specific questions about the use of AI in its 2018 findings [11]. ...
This study constitutes a literature review of empirical research concerning AI and productivity, encompassing agricultural productivity, total factor productivity, and the productivity of publicly listed manufacturing firms. It includes instrumental variable regression analyses of AI and firm-level productivity, AI adoption in the United States, metrics of AI utilisation, determinants of AI adoption, data collection methodologies related to AI, the implications of the "productivity paradox" associated with AI, and the mechanisms through which AI impacts economic growth.
... First, the dynamic restructuring of global green low-carbon industry value chains has intensified technological diffusion barriers and core patent monopolies, driving up R&D costs and constraining independent innovation efficiency [7]. AI technologies, leveraging intelligent data analytics and patent semantic mining algorithms, can effectively identify technological complementarities and reduce information search costs, thereby breaking the "low-end lock-in" dilemma in supply chain restructuring [8,9]. Second, insufficient inter-organizational knowledge sharing, frequent transaction frictions, and resource misallocation undermine collaborative innovation effectiveness [10]. ...
... The existing research primarily focuses on how AI impacts innovation in the manufacturing and industrial sectors. For instance, Rammer et al. [9] empirically demonstrated, based on data from German industrial firms, that AI technologies significantly promote innovation in product and production process domains. Wang et al. [24] found that AI can replace labor while driving both general and sustainable enterprise innovation, with absorptive capacity acting as a mediating factor. ...
... First of all, AI-driven data mining and analytics capabilities can break through the geographical and organizational boundaries of traditional innovation networks. For example, a knowledge graph technology based on a machine learning algorithm can dynamically match heterogeneous knowledge resources and promote the connection of innovation subjects across regions and fields [9,22]. Secondly, artificial intelligence reduces information asymmetry and goal conflict in industryuniversity-research collaboration by standardizing data interfaces and intelligent decisionmaking systems. ...
This study focuses on the intrinsic mechanisms and sustainable value of artificial intelligence (AI)-driven green collaborative innovation in enterprises amid the global green low-carbon transition, revealing new pathways for digital technology-enabled green development. Based on the data of China’s A-share listed companies jointly applying for green patents with other entities from 2010 to 2023, this study used a high-dimensional fixed effect model to empirically find that artificial intelligence significantly promotes green collaborative innovation. This promoting effect proved more pronounced in the case of high macroeconomic uncertainty, large enterprises and SOEs. A mechanism test revealed that artificial intelligence drives green collaborative innovation primarily by reducing transaction costs and optimizing the labor structure. A moderating effect analysis showed that green investor entry and CEO openness can strengthen the facilitating effect of artificial intelligence on green collaborative innovation. In addition, the facilitating effect of artificial intelligence on green collaborative innovation helps companies reduce carbon emissions and improve ESG performance, driving the transformation of business ecosystems toward environmental sustainability. From a technology–organization–environment co-evolution perspective, this research clarifies the micro-level operational chain of AI-enabled green innovation, providing theoretical support for developing countries to achieve leapfrog low-carbon transitions through digital technologies. Practically, it offers actionable insights for advancing AI-enabled green industries, constructing collaborative green innovation ecosystems, and supporting the realization of the United Nations Sustainable Development Goals (SDGs).
... Two papers authored by Zhu K hold the top positions in local citations, with four citations for the paper by Zhu et al. [65], and three citations for the paper by Zhu and Kraemer [15]. Both the papers by Rammer et al. [66] and Chen et al. [67] have received two local citations each. Figure 4 illustrates the historiographic mapping of 27 nodes based on co-citations. ...
... This visual representation facilitates the identification of historical paths, which highlight distinct research topics and their foundational authors or documents [62]. Notably, the works of Adner and Levinthal [70], Forman [37], Zhu and Kraemer [15], Boland et al. [71], Baird et al. [72], Hengstler et al. [35], Cennamo et al. [68], and Rammer et al. [66] have contributed to the development of new research themes. Figure 5 depicts the co-occurrence network generated from the abstract using bigrams to capture detailed insights. ...
... Table 11. Technological antecedents [1, 3,5,11,12,[15][16][17][18]20,21,27,35,37,[40][41][42]45,65,66,74,80,87,[119][120][121]. ...
The rapid proliferation of digital technologies across industries has created significant opportunities for enterprises, yet a comprehensive and integrated review of digital technology diffusion (DTD) at the firm level remains underexplored. This study addresses this gap by systematically analyzing 87 research articles published between 1993 and June 2024 in top-tier management and business journals (ranked A* or A in the ABDC-JQL 2022), sourced from the Web of Science database. Employing a hybrid review methodology that combines bibliometric and framework-based reviews, this study provides a comprehensive overview of the existing research on firm-level DTD. By leveraging the established theories, contexts, methods, antecedents, decisions, and outcomes (TCM-ADO) framework, we consolidate fragmented insights and propose future research directions. This study pioneers the application of the TCM-ADO framework to DTD, offering a novel taxonomy that advances theoretical development.
... AI refers to the use of machine learning, computer vision, deep learning, and other technologies to imitate human behavior, thereby achieving the replacement of human or mental labor (Liu et al. 2022). AI is a technology that uses machines to replace some human functions, which can automate the production process, improve operational quality, and enhance products and services (Rammer et al. 2022). As a new general-purpose technology (Akter et al. 2024), AI has been widely applied in various aspects such as industrial production, transportation, and service industries. ...
... Many scholars have studied the impact of AI on the green economy . Among them, one view holds that AI can promote technological innovation and sustainable development, and AI has replaced the market of low-skilled traditional labor, mainly reflected in the application of industrial robots (Rammer et al. 2022;Almuaythir et al. 2024). Another view is that AI has not improved production efficiency and may lead to the productivity paradox, which is not conducive to economic growth (Kalai et al. 2024). ...
The explosive growth of the Artificial Intelligence market signifies the acceleration of a new phase in the industrial revolution. The challenges of global climate change and rapid technological evolution necessitate innovative approaches to improve carbon emission efficiency. While advancements in renewable energy and carbon capture technologies have been widely explored, the transformative potential of artificial intelligence in optimizing carbon emission efficiency remains underexamined. Based on the data of 285 Chinese cities from 2010 to 2022, this study examines the impact and mechanism of artificial intelligence on carbon emission efficiency through the Spatial Durbin Model (SDM). The research findings indicate that the development of artificial intelligence has effectively promoted the improvement of carbon emission efficiency, and the reduction effect remains consistent across different spatial weights. In terms of mechanism analysis, both technological research and development innovation and environmental policies have enhanced carbon emission efficiency. Finally, some suggestions are put forward to promote the development of artificial intelligence and energy conservation and emission reduction in China.
... However, the impact of AI assimilation on entrepreneurial performance, particularly for women entrepreneurs, is under-researched, warranting more attention from both practitioners and researchers (Al-Attari et al., 2024;Iram et al., 2022;Zhang et al., 2021). Evolving literature acknowledges that AI enhances business value, increases entrepreneurial output and performance (Yang, 2022), boosts innovative productivity (Bag et al., 2021), and helps forecast entrepreneurial opportunities and adapt to macro-level dynamics (Jabeur et al., 2022;Rammer et al., 2022). Saudi Arabia's Vision 2030 reflects a strong political resolve to empower women, elevate their economic participation, and enhance their capacities and knowledge (Bullough et al., 2015;Al-Attar et al., 2024;Iram and Bilal, 2023). ...
... Additionally, Chen et al. (2017) found a positive correlation between AI capabilities and entrepreneurial performance in e-commerce firms. Rammer et al. (2022) also suggested a positive relationship between AI and entrepreneurial performance using firm-level data from Germany. The intricacy of Saudi women entrepreneurs' experiences and agility in the presence of gender bias and internationalizing socio-cultural values and AI technology adoption during the formation of their entrepreneurial identities also have an impact on their entrepreneurial performance (Alreshoodi et al., 2022;Iram and Bilal, 2023;Khan and Nadeem, 2022). ...
Purpose
This research aligns with Saudi Vision 2030 and aims to investigate the impact of AI assimilation on entrepreneurial performance, focusing on the mediating role of entrepreneurial agility and the moderating effect of AI proficiency, guided by the theory of technology dominance (TTD).
Design/methodology/approach
Data were collected from a sample of 390 women entrepreneurs in Riyadh, Saudi Arabia, engaged in small and medium-sized enterprises within low-technology service industries, where the adoption of intelligent decision aids is on the rise. The dataset was analyzed by using the structural equation modeling (PLS-SEM) technique to ensure robust empirical validation and hypothesis testing.
Findings
The findings indicate that AI assimilation enhances entrepreneurial performance through increased agility. However, AI assimilation alone is insufficient for achieving optimal agility and improved entrepreneurial performance; the proficiency of the entrepreneur in AI is also crucial. When an entrepreneur’s skills and expertise align with AI assimilation, their agility is significantly enhanced, leading to better entrepreneurial performance.
Originality/value
In line with the objectives of Saudi Vision 2030, this study emphasizes how crucial it is for Saudi Arabian women’s enterprises to embrace AI to improve their agility and decision-making. To guarantee alignment with organizational systems, which will increase innovation and workforce involvement, entrepreneurs should place a high priority on AI expertise. To assist these initiatives, policy improvements should concentrate on AI education, certifications, training and funding.
... While AI has consistently been linked to positive organizational outcomes, providing a strategic advantage for firms of all sizes, the dynamics of AI implementation and its outcomes can differ significantly between small and medium enterprises (SMEs) and larger, wellestablished firms. These differences result from variations in resources, expertise, cost structure, and support systems, among other factors (Abrokwah-Larbi & Awuku-Larbi, 2024;Czarnitzki et al., 2023;Damioli et al., 2021;Kopka & Fornahl, 2024;Rammer et al., 2022;Schwaeke et al., 2024;Wamba-Taguimdje et al., 2020). For instance, Schwaeke et al. (2024) examined the current state of AI adoption in SMEs through a systematic review and found that infrastructure, culture, compatibility, and regulations are key factors influencing AI adoption. ...
... From automating repetitive operations to enhancing human abilities in complicated settings including image identification, processing, decision-making, natural language processing, and speech synthesis, AI spans a wide range of sectors and applications. In business, the potential impact is vast, influencing functions such as marketing (Abrokwah-Larbi & Awuku-Larbi, 2024;Kumar et al., 2024), production (Chatterjee et al., 2021c;Merhi & Harfouche, 2024), human resources (Li et al., 2023;Kapoor, 2024;Vedapradha et al., 2024), security (Rawindaran et al., 2022), and innovation activities and beyond (Feng et al., 2024;Rammer et al., 2022). ...
Artificial intelligence (AI) has quickly emerged as a top technological priority for companies in various sectors, radically altering business operations. However, the existing literature reveals a fragmented and inconsistent understanding of AI adoption dynamics between small and medium enterprises (SMEs) and larger, well-established firms. This dichotomy of the existing research raises important questions about whether the AI tools and application modalities used by these companies are inherently similar or if significant differences exist in their implementation and outcomes due to varying organizational sizes. This study evaluates whether small and large firms’ efforts toward implementing AI differ significantly using bibliometric analysis and a systematic literature review from the Web of Science and Scopus databases. A total of 78 peer-reviewed articles were analyzed and categorized states and trends into 10 dimensions: (1) technology readiness, (2) customization, (3) AI tools and needs, (4) data requirements, (5) skills and competencies, (6) financial readiness, (7) management support, (8) market and competitive pressure, (9) partnership and collaboration, and (10) regulatory compliance, based on the technology–organization–environment (TOE) theoretical model. A bibliometric mapping approach was adopted to visualize bibliometric data using VOSviewer. The review brings together collective insights from several leading expert contributors to emphasize areas where SMEs need additional support to fully leverage AI technologies. The results provide pragmatic insights for policymakers, helping them develop tailored approaches for both SMEs and large enterprises to meet their unique needs while acknowledging AI's undeniable role in competitiveness and growth.
... Their measure of AI includes various AI methods (such as language understanding, image recognition, machine learning, knowledge-based systems) and is not restricted to AI methods developed internally. They model AI as an intangible capital rather than as an innovation, but in an early companion paper (Rammer et al., 2022) they found evidence of a positive correlation between the use of AI and various measures of innovation, predominantly world-first innovations. Bartz-Zuccala et al. (2018) introduce management practices besides R&D as determinants of innovation. ...
... Not quite in the spirit of the CDM model, some studies have included additional determinants of innovation but treated them as exogenous:Gaglio et al. (2022) andLo et al. (2023) for digital technologies,Kijek and Kijek (2019) for ICT, training and investment in machinery and equipment,Rammer et al. (2022) for artificial intelligence. ...
This paper reviews the empirical work that has been done over the period 2013–2023 on the topic of innovation and productivity. A visual graph based on keywords shows the main areas that have been investigated. The literature review is organized around the way the link between innovation and productivity has been analyzed, the data that have been used, and the evidence that has been obtained. The paper ends with suggestions of future research on the topic.
... Moreover, businesses that have used AI for a longer time produce more innovative solutions and come up with new ones when compared to traditional business settings (Rammer et al., 2022). ...
This research study aimed to examine the perceptions of EFL Instructors on AI in
education and AI integration into EFL instruction. An instrumental case study methodology was used in this qualitative research design. Purposive sampling was
used to choose the sample and included 10 EFL Instructors working at a private
university in Türkiye. Semi-structured interviews were used to collect data. All data
was audio-recorded and then transcribed. Analysis of the data was carried out by
using the software program MAXQDA. An inductive thematic analysis method was
utilized in the data analysis process and the findings were presented with three main
themes: perceptions of AI in education, perceptions of AI in EFL context, and future
of AI in EFL. The results indicated that the EFL instructors are aware of the
existence of AI. However, they had limited variability with specific AI tools
designed for educational purposes and have not yet incorporated AI into their
English course teaching. Most of the instructors have positive perceptions on the
integration of AI in EFL and they mentioned that AI might reduce their workload
and save their time. They also stated that AI can assist students to improve their
language skills, create personalized learning environments and enhance learner
autonomy. However, instructors have some ethical, technical and pedagogical
concerns about integrating AI in EFL. To eliminate these concerns, they highlighted
the significance of trainings and suggested that there should be well-structured, self- paced and hands-on-learning trainings both for them and students.
Keywords: AI, AI tools, English Language Teaching, instructional design
... National initiatives, such as the Digital Transformation Plan 2030, should be in line with incentives for the adoption of AI, such as grants for fintech and health-tech businesses. Egypt lags behind India and Germany in tech-driven businesses, demonstrating that academics can bridge theoretical and practical gaps by including AI marketing courses in entrepreneurship curricula (Rammer, 2022). In order for startups to stay competitive in Africa's $1.3 trillion digital market, these actions are essential to establishing Egypt as a regional center for AI innovation. ...
This study systematically reviews global practices in AI-enabled digital marketing for startups and examines the implementation challenges faced by Egyptian entrepreneurs. By systematically reviewing 21 studies (2017–2023) from Scopus, Web of Science, and Science Direct, the research highlights how AI enhances engagement with customers, improves campaign results, and boosts prediction abilities. Key findings reveal that AI tools, such as chatbots and predictive analytics, improve personalization, with some studies reporting an increase in conversion rates in emerging markets. However, Egyptian startups face significant barriers, including limited AI adoption, infrastructural gaps, and skill shortages. The study also examines Egypt’s entrepreneurial ecosystem, noting government-backed incubators like INTILAC and a youth-driven, tech-savvy population as potential enablers for AI integration. Challenges such as ethical concerns, algorithmic bias, and cultural readiness still persist. The paper concludes with recommendations for policymakers and entrepreneurs to bridge these gaps, emphasizing the need for AI literacy, targeted incentives, and ethical frameworks to foster sustainable growth in Egypt’s digital economy.
... Other measures involve the number of patent citations as an indicator of the level of innovativeness [41] and the diversity of a firm's patents across technological fields as an indicator of innovation diversity [42]. Additionally, when available, some studies incorporate survey responses to assess innovation activities and perceptions directly from firms [43]. However, the most common approaches involve the spending on R&D, either as absolute amount of total investment [44] or, more commonly, as a ratio to total sales (or turnover, used synonymously), referred to as 'R&D intensity' [45], which is also used in this study. ...
The prevailing narrative in the management literature views R&D as a high-risk, high-return activity. Although firms with varying risk-return preferences pursue R&D, this conventional perspective continues to influence decision-making in both corporate strategy and economic policy. This paper questions the narrative by using a novel statistical framework that accounts for competitive strategy and environmental turbulences. Drawing on firm innovation data from the Community Innovation Survey (CIS), we apply semiparametric regression for location and scale to model both the mean and the variance of turnover growth as a function of the interaction between R&D intensity and environmental turbulence, across four common competitive strategy regimes. The findings reveal that for firms prioritizing price leadership across a broad product range, R&D is associated with reduced risk and minimal impact on average growth. Only for firms specifically focused on high quality or small product ranges, the results align with prior research, confirming the expected high-risk, high-return relationship associated with R&D.
... Most of those studies found significant impacts on adoption intention (Pillai et al., 2022). Moreover, scholars (Rammer et al., 2022;Pillai and Sivathanu, 2020;Chenet al., 2023) applied TOE framework to evaluate the adoption of AI-based technologies in the hospitality and tourism industry, whereas Fu et al. (2023) took a different approach and prioritized the TOE determinants for adopting AI in the retail industry. Maroufkhani et al. (2020) observed the influence of TOE factors on not only AI-driven big data analytics adoption but also financial and marketing performances in the context of small and medium-sized companies. ...
Purpose
Based on extended technology, organization and environment (TOE) theoretical framework, the purpose of this study is to investigate the influence of generative artificial intelligence (AI) enablers (GAIEs) and generative AI readiness (GAIR) constructs on the generative AI adoption intention (GAII). This exploration is facilitated through the mediation of top management support and moderation of environmental factors (EFs).
Design/methodology/approach
To empirically validate the framework, this study uses PLS-SEM technique. Data is collected from 425 owners and managers from IT companies in Pune, India ensuring adherence to recommended sample size guidelines.
Findings
This study brings light to the intricate relationships between GAIEs, GAIR and GAII, considering the impact of MS and EF. It is found that GAIEs and GAIR factors significantly impact GAII.
Research limitations/implications
This study contributes to theoretical understanding by expanding existing literature using extended TOE framework considering generative AI adoption. This research also holds significance for practitioners seeking insights into the readiness to GAII. However, the data were collected from respondents in Pune (India), which hinders generalizability of the results. The study results depend on cross-sectional data which could have causality and endogeneity defects.
Originality/value
Previous studies have mainly focused on AI or related technologies, leaving a noticeable gap in the literature concerning readiness specifically for generative AI which the present study fills.
... Moreover, in China, firms' productivity has been positively impacted by AI technology innovation (Zhai & Liu, 2023). Meanwhile, AI technologies have been used to enhance innovation processes, and this effort has resulted in approximately 6% of Germany's total annual business cost savings (Rammer et al., 2022). The following hypothesis was proposed: ...
... They also stated that process automation, organizational learning, and process innovation serve as important complementary partial mediators, offering insights into how AI generates business value. This is aligned with the findings of several other authors who claim that AI assimilation and capability positively enhance firm performance (Wamba-Taguimdje et al., 2020;Bag et al., 2021;Wamba, 2022;Chen et al., 2022), innovation performance (Rammer et al., 2022), and organizational creativity (Mikalef & Gupta, 2021). ...
The information technology and business process management sector constitutes a large share of the economy and employment in the Philippines, and recent developments in AI have raised concerns over job displacement and lost revenue in the sector. This report analyzes and shares insights from key statistics, academic research, industry reports, online interviews, and personal interviews with industry experts and government officials. The findings emphasize the need for education and training initiatives and improvements to offset possible job displacement.
... For instance, Niebel et al. (2019) examined the relationship between the use of big data analytics and product innovation at the firm level. Rammer et al. (2022) investigated the relationship between AI and industrial innovation, finding that the use of AI technologies is associated with better results in terms of product and process innovation. A different study found that using digital technologies can enhance energy management practices within a firm (Lange et al., 2020). ...
This study empirically examines the impact of the joint adoption of eco-innovations and digital technologies-referred as "the twin transition"-on firms' sales performance in the Korean manufacturing sector. The analysis, which uses data from the Korea Innovation Survey 2022 and employs an instrumental variable approach, shows significant positive effects on sales for firms that adopt both eco-innovations and digital innovations. Specifically, the twin transition is associated with a range of positive increase in sales in 2021. We also found that sales increased significantly for firms that combined eco-innovation with a subset of digital innovations: AI, big data, IoT, cloud computing, 3D computing, and 5G. These findings highlight the strategic importance for firms to integrate eco-innovation with digital technologies to enhance their business performance. This research underscores the need for policies and incentives that support sustainable growth and competitive advantage by promoting the twin transition.
... Some digital technologies, such as those related to physical production that must be installed "in situ" (that is, on the plant's grounds), allow firms to automate processes, reduce waste, and improve productivity. This category includes technologies such as advanced automated robots (Eurofound, 2018), additive manufacturing (Freund et al., 2022;Laplume et al., 2016), artificial intelligence (Rammer et al., 2022;Yang, 2022), and big data analytics (Niebel et al., 2019). The latter is associated with high-skill labor, innovation activities, and cost savings. ...
This paper explores the relationship between digitalization and the regionalization of Global Value Chains (GVCs) in European industries. The paper discusses the theoretical channels through which digital technologies may influence GVC regionalization and develops a conceptual framework to guide the empirical analysis. The analysis focuses on the manufacturing sectors of a sample of European countries in the period 2005–2018. To econometrically identify the causal effect of digital technologies on regionalization, we implement a Bartik instrumental variables approach exploiting patent data on information and communication technologies (ICT) and artificial intelligence (AI). Our main finding is that digital technologies enhance the intra-EU flows more than the extra-EU ones, thus pointing to the regionalization of GVCs in Europe.
... Artificial intelligence (AI), as a revolutionary general-purpose technology, offers a new approach to shaping SCR in the era of the digital economy. AI mimics human intelligence, allowing computer systems to carry out tasks that resemble human thinking and decision-making processes [5]. According to the 2025 Top 10 Supply Chain Trends Report released by the Association for Supply Chain Management, AI ranks first, becoming the most influential supply chain trend in 2025 [6]. ...
In the volatility, uncertainty, complexity, and ambiguity (VUCA) environment, the application of artificial intelligence (AI) technologies is a key engine for shaping supply chain resilience (SCR). This study employs the entropy method to develop an evaluation index system for SCR, incorporating two key dimensions: resistance and recovery capacity. Using a sample of Chinese-listed enterprises from 2009 to 2022, this study reveals that AI significantly enhances SCR, and CEOs’ sports experience can positively moderate the association between AI and SCR. Mechanism examination shows that AI promotes SCR through operational efficiency optimization, information, and knowledge spillover in the supply chain. Heterogeneity analysis reveals that the positive impact of AI is more significant in firms with a high-skilled labor force, firms with high heterogeneity of the executive team’s human capital, high-tech industries, and regions with strong digital infrastructure. Moreover, the AI application has a diffusion effect on the upstream and downstream enterprises of the supply chain, improving AI adoption levels. Our research not only augments the existing literature on the economic ramifications of AI adoption and the strategic value derived from CEOs’ extramural experience but also offers both theoretical frameworks and empirical insights for executive recruitment and fortifying SCR.
... On the one hand, the wide application of AI technology in various fields, such as transportation and energy production, has made a significant contribution to optimizing energy consumption and reducing waste [33], as such technology is able to collect and process information to help shape a highly integrated man-machine situation. Based on these advantages, AI can promote technological innovation [34], improve labor productivity [35], and optimize resource allocation [36] to improve CEE. On the other hand, the training and operation of AI technology require too much electricity and generate a large amount of carbon emissions [37], resulting in a "rebound effect", which offsets the improved CEE resulting from the development of AI technology and even increases the total amount of carbon emissions. ...
Artificial intelligence (AI) technology not only promotes rapid economic development but also plays an irreplaceable role in improving environmental quality. Based on the quasi-natural experiment of the National Artificial Intelligence Innovation Comprehensive Experimental Zone, this paper empirically studies the effect and mechanism of AI on urban air quality (AQ) using the multi-time difference-in-difference model. The research results showed that AI improved the AQ of cities. The mechanism analysis results indicated that there was a positive mediating effect of government environmental attention on the relationship between AI and AQ improvement. Public environmental attention can further enhance the role of AI in improving urban AQ. Further analysis revealed that the improvement effect of AI on urban AQ was mainly reflected in eastern cities and non-resource-based cities. The research conclusion of this study provides reliable empirical evidence for leveraging AI to empower urban green development and assist in air pollution prevention practices.
Rooted in the idea that machines could mimic cognitive functions associated with the human mind. Today, AI encompasses a variety of technologies and methodologies that empower process information, recognize patterns, and make decisions autonomously or semi-autonomously. language translation, or game playing. These systems operate under a limited set of constraints and excel in their designated areas, often outperforming humans in terms of speed and accuracy. Examples of narrow, its development could revolutionize industries and societal functions. As deep learning architectures continue to evolve, they increasingly enable . Recent advancements in NLP, particularly with transformer models like GPT and BERT, have improved the ability of machines to grasp context and generate coherent text, further blurring the lines between human and machine communication.Computer vision, another critical area of AI, involves teaching machines video analysis, computer vision enables applications ranging from surveillance systems to medical imaging diagnostics.
Edge Computing (EC) is one of the proposed solutions to address the problems that the industry is facing when implementing Predictive Maintenance (PdM) implementations that can benefit from Edge Artificial Intelligence (Edge AI) systems. In this work, we have compared six of the most popular no-code Edge AI frameworks in the market. The comparison considers economic cost, the number of features, usability, and performance. We used a combination of the analytic hierarchy process (AHP) and the technique for order performance by similarity to the ideal solution (TOPSIS) to compare the frameworks. We consulted ten independent experts on Edge AI, four employed in industry and the other six in academia. These experts defined the importance of each criterion by deciding the weights of TOPSIS using AHP. We performed two different classification tests on each framework platform using data from a public dataset for PdM on biomedical equipment. Magnetometer data were used for test 1, and accelerometer data were used for test 2. We obtained the F1 score, flash memory, and latency metrics. There was a high level of consensus between the worlds of academia and industry when assigning the weights. Therefore, the overall comparison ranked the analyzed frameworks similarly, with NanoEdgeAIStudio ranked first when considering all weights, academia only, and industry only. In terms of performance, there is room for improvement in all frameworks, as they did not reach the metrics of the previously developed custom Edge AI solution. We identified some limitations that should be fixed to improve the comparison method in the future, like adding weights to the feature criteria or increasing the number and variety of performance tests.
Purpose
This study aims to examine the relationship between knowledge base and the performance of digital firms, as well as the roles of innovation openness and market volatility in this relationship. Based on the size and structural characteristics of the knowledge base, two dimensions of knowledge stock and knowledge diversity are selected for the study.
Design/methodology/approach
In this study, 334 listed firms in the digital industry in China are used to test the hypotheses using the Ordinary Least Squares model.
Findings
Knowledge stock in the knowledge base promotes the performance of digital firms, while knowledge diversity has an asymmetric inverted U-shaped relationship with the performance of digital firms, indicating a “diversity trap.” Furthermore, innovation openness and market volatility positively moderate the effect of knowledge stock on digital firm performance. They also sharpen the inverted U-shaped relationship between knowledge diversity and performance, exacerbating the “diversity trap” and steepening the curve. Additionally, market volatility encourages firms to achieve over-performance, shifting the curve upward.
Originality/value
This study innovatively focuses on the digital industry and explores the relationship between knowledge base and enterprise performance based on two dimensions: knowledge stock and knowledge diversity. Compared to labor-intensive industries, the digital industry is technology-intensive and highly reliant on knowledge resources. Therefore, examining firms in this industry offers more representative and practical insights. At the same time, a more detailed and deeper study of the economic effects of the knowledge base can complement the research on the knowledge base and propose more targeted countermeasures for a good match between the knowledge base and the environment of digital enterprises.
The integration of agricultural and tourism industries is an important way for underdeveloped regions to build economic development momentum with a distinctive industrial system, and digital technology or artificial intelligence provides opportunities for industrial integration and innovative development. Using panel data from 17 prefecture-level cities in the former Central Soviet Area of Jiangxi, Fujian, and Guangdong Provinces spanning from 2012 to 2021, this study empirically examines the impact of digital intelligence on the integration of agricultural and tourism industries, along with its underlying mechanisms. The empirical results show that digital intelligence can significantly promote the integration of agricultural and tourism industries, but intelligence contributes less to the integration of agricultural and tourism than digitization. In addition, digital intelligence promotes the integration of agricultural and tourism industries through the industrial structure, threshold-lowing, and consumer demand effects. Moreover, the empowering effect of digital intelligence in the cities of the Jiangxi Region is greater than that of the Fujian and Guangdong Regions.
Purpose
Drawing on information processing theory, dynamic capabilities theory and institutional theory, this study aims to explore the impact of artificial intelligence adoption on radical green innovation and examine the mediating role of digital resilience and the moderating role of command-and-control environmental regulation.
Design/methodology/approach
This study adopts a quantitative design and collects data from 273 Chinese manufacturing firms. Multiple regression analysis and bootstrap analysis are employed to test the research hypotheses.
Findings
The results show that artificial intelligence adoption aids firms in achieving radical green innovation and enables firms to develop proactive digital resilience and reactive digital resilience. Furthermore, both proactive digital resilience and reactive digital resilience play a partial mediating role in the relationship between artificial intelligence adoption and radical green innovation. Moreover, command-and-control environmental regulation positively moderates the relationship between artificial intelligence adoption and radical green innovation.
Originality/value
This study unpacks the black box of the driving mechanism for radical green innovation through artificial intelligence adoption by investigating the role of two types of digital resilience. Furthermore, by demonstrating the moderating role of command-and-control environmental regulation, this study expands the boundary condition research on artificial intelligence adoption.
Intelligent transformation is crucial for driving economic development. Since the advent of Industry 4.0, China’s intelligent transformation has advanced rapidly, drawing attention to its impacts on economic resilience. What are the heterogeneities in the impacts at different times and in different cities? What factors influence its effects? To address these questions, based on the panel data set of prefecture-level cities in China from 2007 to 2019, we conducted analyses using the Vertical and Horizontal Scatter Degree (VHSD) method, the Bartik IV method, the Panel Geographically and Temporally Weighted Regression (PGTWR) model, and the correlation coefficient method, and revealed the following key findings: (1) during the study period, both intelligent transformation and economic resilience of Chinese cities increased, displaying a spatial pattern of “East high-West low”; (2) intelligent transformation improved economic resilience, its effect was weaker during the crisis period and showed a fluctuating yet rise during the recovery period; (3) the resilience effect of intelligent transformation showed a spatial pattern, decreasing from “Southeast to Northwest”. In most cities, the effect size exhibited diminishing marginal effects; (4) the rationalization of industrial structure and increased investments in science and technology directly contributed to economic resilience, and also amplified the effect of intelligent transformation. These findings contribute to the understanding of the resilience effects of intelligent transformation and provide references for local governments to formulate differentiated strategies for enhancing economic resilience. In addition, the research framework of this paper also offers referable research ideas for other countries to conduct studies on the economic resilience effects of intelligent transformation.
The digital economy has become an important engine for global economic development by promoting optimal resource allocation and advancing industrial restructuring. Based on the panel data from 279 prefecture-level cities in China from 2012 to 2021, this paper constructs the spatial association networks of urban digital economy using a modified gravity model and analyzes the complex network characteristics and driving factors of urban digital economy growth by the social network analysis methods and the Quadratic Assignment Procedure (QAP). This study finds that (1) the level of urban digital economy in China shows a rising trend year by year and displays an uneven spatial distribution. (2) Spatial association networks of urban digital economy are relatively well-connected, with increasing density and stability of spatial associations, yet some hierarchical structure remains, and overall connectivity still needs to be improved. (3) Most cities in the east region occupy the core positions within the complex network, significantly influencing the overall complex network through a “siphon effect”, while cities in the central region play more of a “bridge” role in the spatial association network. In contrast, cities in the northwest, northeast, and southwest regions are situated on the periphery of this spatial association network. (4) The economic development level, informatization level, technological innovation, urbanization level, industrial structure, and human capital contribute to the formation of the spatial association network of the digital economy. Based on these conclusions, specific policy implications for the future development of the spatial association network of the urban digital economy are proposed.
Purpose
This study examines the impact of artificial intelligence (AI) adoption on employee proactive behavior in South Korean organizations. Drawing on social cognitive theory, we investigate the mediating role of job reflection and the moderating effect of coaching leadership in this relationship. Our research aims to address the critical gap in understanding how AI adoption influences employee proactivity, a crucial behavior in dynamic, technology-driven environments. By exploring the cognitive processes and leadership factors involved, we seek to provide insights into how organizations can leverage AI adoption to foster proactive behavior while emphasizing the importance of reflective practices and supportive leadership.
Design/methodology/approach
We employed a time-lagged survey with 405 employees across three waves. AI adoption, coaching leadership and control variables were measured at Time 1, job reflection at Time 2 and proactive behavior at Time 3. Hierarchical regression and bootstrapping analyses were employed to test hypotheses.
Findings
Our results reveal that AI adoption positively relates to proactive behavior, with job reflection mediating this relationship. While coaching leadership moderates the relationship between AI adoption and job reflection, the hypothesized moderated mediation effect was not supported, suggesting job reflection’s mediating role remains consistent regardless of leadership level. This finding highlights the robustness of job reflection as a cognitive mechanism in the AI adoption context.
Originality/value
This study advances understanding of the mechanisms linking AI adoption to proactive behavior by identifying job reflection as a key mediating process. It provides insights into how organizations can leverage cognitive processes to enhance employee proactivity during AI implementation while revealing the complexity of leadership’s role in this relationship.
Artificial intelligence (AI) technologies have gained significance for all types of firms, including family firms. However, unlike other business contexts, family firms face unique challenges in adopting complex and generative technologies, such as AI. This is particularly evident when examining the heterogeneous nature of family firms distinguished by passive (i.e., family ownership) and active (i.e., family management) family involvement. Specifically, we adopt a social capital perspective and combine inductive qualitative and deductive quantitative research into a mixed-methods design. In the first step, we conduct a multiple case study of eight firms using rich data from 112 interviews and archival documents. While the case evidence shows that strong external network ties with suppliers, customers, and competitors help overcome the challenges related to AI technologies, we reveal opposing impacts of passive and active family involvement. Interestingly, our qualitative insights emphasize a negative moderation effect for passive family ownership exacerbating the challenges of AI and a positive moderation effect for active family management facilitating AI adoption. In the second step, we conduct a quantitative test of this conceptual model. On the basis of large-scale data from 1,444 firms, we find empirical support that increasing strengths of external network ties with suppliers, customers, and competitors drive AI adoption. Moreover, we find support for the opposing roles of passive family ownership as a negative moderator and active family management as a positive moderator in the relationship between supplier ties on the one hand and AI adoption on the other. Our study contributes toward a nuanced understanding of the idiosyncratic challenges of AI adoption faced by different types of family firms and the role of social capital in this regard and, more broadly, to research on innovation and technology adoption in family firms.
This study explores the role of perceived utility, social influence, and ethical concerns in the adoption of AI-based data analysis tools among academic researchers in China, focusing on differences between public and private universities. The research aims to identify key drivers and barriers influencing the integration of AI technology in academic settings. A quantitative approach was employed, using a multi-group structural equation model (SEM) analysis to assess data collected from 750 academic researchers across various disciplines (Npvt = 402; Npub = 348). The findings reveal that both perceived utility and social influence significantly influence the adoption of AI tools. Higher perceived utility and stronger social influence lead to greater adoption. However, ethical concerns were found to moderate these relationships, particularly in public universities, where researchers with high ethical concerns perceived greater risks, thereby reducing their likelihood of adoption. In contrast, private university researchers showed a higher tolerance for perceived risks when utility and social influence were evident. The study’s implications suggest that to promote AI adoption, institutions must address ethical concerns and perceived risks, particularly in public universities, by enhancing transparency, providing ethical guidelines, and offering comprehensive training. These efforts can lead to more effective integration of AI technologies, ultimately enhancing research productivity and innovation across diverse academic environments.
The aging global workforce poses significant challenges for organizations striving to sustain productivity amidst the physical and cognitive decline associated with aging. Artificial intelligence (AI) emerges as a transformative tool, offering solutions to enhance the capabilities of older employees, automate routine tasks, and support informed decision-making. This paper explores the potential of AI to counteract productivity losses linked to workforce aging through a comprehensive review of existing research. It synthesizes insights on the effects of aging on employee productivity, examines how AI can complement and augment the contributions of older employees, and evaluates the broader implications of AI integration on organizational efficiency. By exploring exploring the interplay between AI’s potential and workforce aging and providing recommendations for inclusive AI implementation, the paper aims to contribute to the ongoing discourse on how AI can foster a sustainable, productive, and inclusive work environment in the face of demographic changes.
As the advantages of AI-driven development continue to emerge, innovative enterprises are increasingly using exploratory innovation, developing new business models, and integrating AI to improve corporate capabilities and optimize performance. This study surveyed 174 practitioners in China's construction industry, using quantitative analysis and PLS-SEM methods to explore the endogenous driving force in AI assimilation and the interactive relationship between exploratory innovation and new business models. The results show that exploratory innovation improves performance through AI assimilation, as does the new business model. In addition, novel business models promote AI assimilation through corporate exploratory innovation. The study found that competitive intensity strengthens the connection between exploratory innovation and AI assimilation but weakens the connection between AI assimilation and performance. Based on these insights, this study proposes a research framework for improving performance through exploratory innovation, novel business model, and AI assimilation under competitive pressure and provides practical management suggestions.
Fulfilling corporate public responsibilities to employees is crucial for sustainable social development. The impact of digital transformation on total factor productivity (TFP) about these responsibilities remains unclear. Taking Chinese manufacturing companies as the research sample, empirical analyses including fixed effect model, propensity score matching, and staggered Differences-in-Differences methods have demonstrated that digital strategy can boost manufacturing companies’ TFP. Further, stepwise regression analysis of the mechanisms revealed that while the transformation primarily boosts productivity directly, it also indirectly boosts TFP by reinforcing employee and public responsibilities. These findings suggest that maintaining responsibilities to employees and the public during digital transformation can enhance corporate productivity. This study sheds light on how digital technology drives corporate social responsibility for sustainable social development.
Purpose
Open-source communities are platforms that promote knowledge sharing. The mitigation of open-source risks is crucial to these communities. Therefore, this article explores the governance mechanisms of knowledge sharing in open-source communities.
Design/methodology/approach
To answer the core research question – “What are the governance mechanisms of knowledge sharing in open-source communities?” – we conducted an in-depth case study analysis of two open-source communities based in China.
Findings
Two types of open-source communities were found: technology-driven communities and enterprise ecosystem-oriented communities. Hence, their governance mechanisms differed. For the former type, it was important to integrate social and commercial value to encourage knowledge exchange and enhance business scenarios through community-user experience. For the latter type, mutual collaboration and knowledge sharing could be fostered through differentiated layouts and the distributed collaboration of developers around data-driven innovation scenarios. This required the integration of individual and ecosystem value through value exchange.
Originality/value
This study advances our understanding of the coordinated development between founding firms and digital technology-based open-source communities. The findings offer important guidance to business practitioners seeking to manage knowledge-sharing activities during digital transformations.
Can artificial intelligence (AI) raise productivity? If we regard AI as a combination of software, hardware, and database use, then it can be modelled as a combination of the deployment of intangible and tangible assets. Since some are measured and some are not, then conventional productivity analysis might miss the contribution of AI. We set out whether there is any evidence to support this view.
Research Summary
We create and validate a new measure of an occupation's exposure to AI that we call the AI Occupational Exposure (AIOE). We use the AIOE to construct a measure of AI exposure at the industry level, which we call the AI Industry Exposure (AIIE) and a measure of AI exposure at the county level, which we call the AI Geographic Exposure (AIGE). We also describe several ways in which the AIOE can be used to create firm level measures of AI exposure. We validate the measures and describe how they can be used in different applications by management, organization and strategy scholars.
Managerial Summary
Although artificial intelligence (AI) promises to spur economic growth, there is widespread concern that it could displace workers, alter industry trajectories, and reshape organizations. Despite the interest in this area, we have limited ability to study the effects of AI on occupations, firms, industries, and geographies because of limited availability of data that measures exposure to AI. To address this limitation, we create and validate a new measure of an occupation's exposure to AI that we call the AI Occupational Exposure (AIOE). We use the AIOE to construct a measure of AI exposure at the industry level (AIIE) and county level (AIGE). We describe how our measures can be useful to scholars and policy‐makers interested in identifying the effect of AI on markets.
Artificial Intelligence (AI) reshapes companies and how innovation management is organized. Consistent with rapid technological development and the replacement of human organization, AI may indeed compel management to rethink a company's entire innovation process. In response, we review and explore the implications for future innovation management. Using ideas from the Carnegie School and the behavioral theory of the firm, we review the implications for innovation management of AI technologies and machine learning-based AI systems. We outline a framework showing the extent to which AI can replace humans and explain what is important to consider in making the transformation to the digital organization of innovation. We conclude our study by exploring directions for future research.
Technical advancements within the subject of artificial intelligence (AI) leads towards development of human-like machines, able to operate autonomously and mimic our cognitive behavior. The progress and interest among managers, academics and the public has created a hype among many industries, and many firms are investing heavily to capitalize on the technology through business model innovation. However, managers are left with little support from academia when aiming to implement AI in their firm’s operations, which leads to an increased risk of project failure and unwanted results. This paper aims to provide a deeper understanding of AI and how it can be used as a catalyst for business model innovation. Due to the increasing range and variety of the available published material, a literature review has been performed to gather current knowledge within AI business model innovation. The results are presented in a roadmap to guide the implementation of AI to firm’s operations. Our presented findings suggest four steps when implementing AI: (1) understand AI and organizational capabilities needed for digital transformation; (2) understand current BM, potential for BMI, and business ecosystem role; (3) develop and refine capabilities needed to implement AI; and (4) reach organizational acceptance and develop internal competencies.
This paper identifies and measures AI-related developments in science, algorithms and technologies using information from scientific publications, open source software (OSS) and patents. A three-pronged approach relying on established bibliometric and patent-based methods, and machine learning (ML) implemented on purposely collected OSS data, unveils a marked increase of AI-related developments over time. Since 2015, AI-related publications increased at 23% a year; in 2014-18, OSS commits related to AI grew about three times as other OSS contributions; in 2017 the share of AI-related IP5 patent families averaged more than 2.3%. The growing role of China in the AI space emerges throughout.
Artificial intelligence (AI) is playing a major role in the new paradigm shift occurring across the technological landscape. After a series of alternate seasons starting in the 60s, AI is now experiencing a new spring. Nevertheless, although it is spreading throughout our economies and societies in multiple ways, the absence of standardised classifications prevents us from obtaining a measure of its pervasiveness. In addition, AI cannot be identified as part of a specific sector, but rather as a transversal technology because the fields in which it is applied do not have precise boundaries. In this work, we address the need for a deeper understanding of this complex phenomenon by investigating economic agents’ involvement in industrial activities aimed to supply AI-related goods and services, and AI-related R&D processes in the form of patents and publications. In order to conduct this extensive analysis, we use a complex systems approach through the agent-artifact space model, which identifies the core dimensions that should be considered. Therefore, by considering the geographic location of the involved agents and their organisation types (i.e., firms, governmental institutions, and research institutes), we (i) provide an overview of the worldwide presence of agents, (ii) investigate the patterns in which AI technological subdomains subsist and scatter in different parts of the system, and (iii) reveal the size, composition, and topology of the AI R&D collaboration network. Based on a unique data collection of multiple micro-based data sources and supported by a methodological framework for the analysis of techno-economic segments (TES), we capture the state of AI in the worldwide landscape in the period 2009–2018. As expected, we find that major roles are played by the US, China, and the EU28. Nevertheless, by measuring the system, we unveil elements that provide new, crucial information to support more conscious discussions in the process of policy design and implementation.
There are numerous emerging studies addressing big data and its application in different organizational aspects, especially regarding its impact on the business innovation process. This study in particular aims at analyzing the existing relationship between Big Data Analytics Capabilities and Co-innovation. To test the hypothesis model, structural equations by the partial least squares method were used in a sample of 112 Colombian firms. The main findings allow to positively relate Big Data Analytics Capabilities with better and more agile processes of product and service co-creation and with more robust collaboration networks with stakeholders internal and external to the firm.
Artificial intelligence (AI) has been altering industries as evidenced by Airbnb, Uber and other companies that have embraced its use to implement innovative new business models. Yet we may not fully understand how this emerging and rapidly advancing technology influences business model innovation. While many companies are being made vulnerable to new competitors equipped with AI technology, this study attempts to focus on the proactive side of the use of AI technology to drive business model innovation. Describing AI technology as the catalyst of business model innovation, this study sheds light on contingent factors shaping business model innovation initiated by the emerging technology. This study first provides a brief overview of AI, current issues being tackled in developing AI and explains how it transforms business models. Our case study of two companies that innovated their business models using AI shows its potential impact. We also discuss how executives can create an innovative AI-based culture, which rephrases the process of AI-based business model innovation. Companies that successfully capitalize on AI can create disruptive innovation through their new business models and processes, enabling them to potentially transform the global competitive landscape.
This introduction to this special issue discusses artificial intelligence (AI), commonly defined as “a system’s ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.” It summarizes seven articles published in this special issue that present a wide variety of perspectives on AI, authored by several of the world’s leading experts and specialists in AI. It concludes by offering a comprehensive outlook on the future of AI, drawing on micro-, meso-, and macro-perspectives.
What if artificial intelligence (AI) machines became teammates rather than tools? This paper reports on an international initiative by 65 collaboration scientists to develop a research agenda for exploring the potential risks and benefits of machines as teammates (MaT). They generated 819 research questions. A subteam of 12 converged them to a research agenda comprising three design areas – Machine artifact, Collaboration, and Institution – and 17 dualities – significant effects with the potential for benefit or harm. The MaT research agenda offers a structure and archetypal research questions to organize early thought and research in this new area of study.
While the disruptive potential of artificial intelligence (AI) and Big Data has been receiving growing attention and concern in a variety of research and application fields over the last few years, it has not received much scrutiny in contemporary entrepreneurship research so far. Here we present some reflections and a collection of papers on the role of AI and Big Data for this emerging area in the study and application of entrepreneurship research. While being mindful of the potentially overwhelming nature of the rapid progress in machine intelligence and other Big Data technologies for contemporary structures in entrepreneurship research, we put an emphasis on the reciprocity of the co-evolving fields of entrepreneurship research and practice. How can AI and Big Data contribute to a productive transformation of the research field and the real-world phenomena (e.g., "smart entrepreneurship")? We also discuss, however, ethical issues as well as challenges around a potential contradiction between entrepreneurial uncertainty and rule-driven AI rationality. The editorial gives researchers and practitioners orientation and showcases avenues and examples for concrete research in this field. At the same time, however, it is not unlikely that we will encounter unforeseeable and currently inexplicable developments in the field soon. We call on entrepreneurship scholars, educators, and practitioners to proactively prepare for future scenarios.
This paper analyzes the relationship between firms' use of big data analytics and their innovative performance in terms of product innovations. Since big data technologies provide new data information practices, they create novel decision-making possibilities, which are widely believed to support firms' innovation process. Applying German firm-level data within a knowledge production function framework we find suggestive evidence that big data analytics is a relevant determinant for the likelihood of a firm becoming a product innovator as well as for the market success of product innovations. These results hold for the manufacturing as well as for the service sector but are contingent on firms' investment in IT-specific skills. Overall, the results support the view that big data analytics have the potential to enable innovation.
Businesses have classically put emphasis on human bonds related to their BM’s [www.conansence.org]. By the fast development of more sensoring, persuasive and virtual BMs increasingly run autonomously by machines, businesses should expect to be able to, build competence and thereby be capable in the future to innovated BM’s and operate BM’s in new types Business Model Ecosystems (BMES) (Lindgren and Rasmussen in J Multi BMI 4:1, 2016) in the future—where physical, digital and virtual BMES become integrated. This will investable open up to new multi business model potential but also require that businesses operate and innovate their multitudes of BM’s differently. BMES and BM’s (Lindgren in J Multi Bus Model Innov Technol 4:1, 2016; Lindgren and Rasmussen in J Multi Bus Model Innov Technol 1: 135, 2013) have for a longtime been based and built up with mainly human bond communication, but new technologies very much based on machine to human communication and machine to machine communication evolves and change the game of BMI with exponential speed. How will this change the game of Business Model Innovation (BMI) between humans, humans and machines and machines to machines. How will this evolvement influence businesses ability to “download”, “see”, “sense”, “relate” and “receive” and relate BM’s with their AS IS and TO BE BM’s. The paper addresses the exponential development of artificial intelligence technologies, persuasive technologies, virtual technologies and thereby increase the potential to create, capture, deliver, receive and consume physical, digital, persuasive and virtual BMs in Business model innovation and introduce a conceptual model to future business model innovation and operation.
Recent technological advances have enabled the emergence of novel platform business models based on digital marketplaces. Marketplaces like Airbnb or Uber offer platforms to connect previously
unmatched demand-side and supply-side participants through innovative forms of value creation, delivery and capture. While countless firms claim to offer the next 'Airbnb for X' or 'Uber for Y', we lack knowledge
about the defining characteristics of these business models. To close the gap, this paper provides a conceptually and empirically grounded taxonomy of marketplace business models. Applying a mixed methods approach, we first develop an integrative framework that integrates the value creation, delivery, and capture choices. Guided by the framework, the research systematically analyzes 100 randomly selected marketplaces with content analysis and binary coding. The gathered data is analyzed with cluster analysis techniques to develop a taxonomy for digital marketplace business models. The clustering process reveals six clearly
distinguishable types of digital marketplace business models and thus shows that there exists no one-size-fits-all approach to creating, delivering, and capturing value with digital marketplaces. We characterize these distinctive business model types by integrating the qualitative and quantitative insights to advance the understanding of platform business models.
The authors reflect on management of big data by organizations. They comment on service level agreements (SLA) which define the nature and quality of information technology services and mention big data-sharing agreements tend to be poorly structured and informal. They reflect on the methodologies of analyzing big data and state it is easy to get false correlations when using typical statistical tools in analyzing big data. They talk about the use of big data in management and behavior research.
There is considerable and growing interest in the emergence of novel technologies, especially from the policy-making perspective. Yet as an area of study, emerging technologies lacks key foundational elements, namely a consensus on what classifies a technology as ’emergent’ and strong research designs that operationalize central theoretical concepts. The present paper aims to fill this gap by developing a definition of ’emerging technologies’ and linking this conceptual effort with the development of a framework for the operationalisation of technological emergence. The definition is developed by combining a basic understanding of the term and in particular the concept of ’emergence’ with a review of key innovation studies dealing with definitional issues of technological emergence. The resulting definition identifies five attributes that feature in the emergence of novel technologies. These are: (i) radical novelty, (ii) relatively fast growth, (iii) coherence, (iv) prominent impact, and (v) uncertainty and ambiguity. The framework for operationalising emerging technologies is then elaborated on the basis of the proposed attributes. To do so, we identify and review major empirical approaches (mainly in, although not limited to, the scientometric domain) for the detection and study of emerging technologies (these include indicators and trend analysis, citation analysis, co-word analysis, overlay mapping, and combinations thereof) and elaborate on how these can be used to operationalise the different attributes of emergence.
This paper investigates the relationship between product market competition and innovation. It uses the radical policy reforms in the UK as instruments for changes in product market competition, and finds a robust inverted-U relationship between competition and patenting. It then develops an endogenousm growth model with step-by-step innovation that can deliver this inverted-U pattern. In this model, competition has an ambiguous effect on innovation. On the one hand, it discourages laggard firms from innovating, as it reduces their rents from catching up with the leaders in the same industry. On the other hand, it encourages neck-and-neck firms to innovate in order to escape competition with their rival. The inverted-U pattern results from the interplay between these two effects, together with the effect of competition on the equilibrium industry structure. The model generates two additional predictions: on the relationship between competition and the average technological distance between leaders and followers across industries; and on the relationship between the distance of an industry to its technological frontier and the steepness of the inverted-U. Both predictions are supported by the data.
This paper studies the links between productivity, innovation and research at the firm level. We introduce three new features: (i) A structural model that explains productivity by innovation output, and innovation output by research investment: (ii) New data on French manufacturing firms, including the number of European patents and the percentage share of innovative sales, as well as firm-level demand pull and technology push indicators; (iii) Econometric methods which correct for selectivity and simultaneity biases and take into account the statistical features of the available data: only a small proportion of firms engage in research activities and/or apply for patents; productivity, innovation and research are endogenously determined; research investment and capital are truncated variables, patents are count data and innovative sales are interval data. We find that using the more widespread methods, and the more usual data and model specification, may lead to sensibly different estimates. We find in particular that simultaneity tends to interact with selectivity, and that both sources of biases must be taken into account together. However our main results are consistent with many of the stylized facts of the empirical literature. The probability of engaging in research (R&D) for a firm increases with its size (number of employees), its market share and diversification, and with the demand pull and technology push indicators. The research effort (R&D capital intensity) of a firm engaged in research increases with the same variables, except for size (its research capital being strictly proportional to size). The firm innovation output, as measured by patent numbers or innovative sales, rises with its research effort and with the demand pull and technology indicators, either directly or indirectly through their effects on research. Finally, firm productivity correlates positively with a higher innovation output, even when controlling for the skill composition of labor as well as for physical capital intensity.
We discuss the strengths and weaknesses of five alternative innovation indicators: R&D, patent applications, total innovation expenditure and shares in sales taken by imitative and by innovative products as they were measured in the 1992 Community Innovation Survey (CIS) in the Netherlands. We conclude that the two most commonly used indicators (R&D and patent applications) have more (and more severe) weaknesses than is often assumed. Moreover, our factor analysis suggests that there is little correlation between the various indicators. This underlines the empirical relevance of various sources of bias of innovation indicators as discussed in this paper.
The paper proposes an analytical framework for comparing different business models for producing information goods and digital services. It is based on three dimensions that also refer to contrasted literature: the economics of matching, the economics of assembling and the economics of knowledge management. Our framework attempts to identify the principal trade-offs at the core of choices among alternative digital business models, and to compare them in terms of competitiveness and efficiency. It also highlights the role played by users in the production of information goods and competition with pure suppliers.
In this paper, we develop an econometric model to estimate the impacts of Electronic Vehicle Management Systems (EVMS) on the load factor (LF) of heavy trucks using data at the operational level. This technology is supposed to improve capacity utilization by reducing coordination costs between demand and supply. The model is estimated on a subsample of the 1999 National Roadside Survey, covering heavy trucks travelling in the province of Quebec. The LF is explained as a function of truck, trip and carrier characteristics. We show that the use of EVMS results in a 16 percentage points increase of LF on backhaul trips. However, we also find that the LF of equipped trucks is reduced by about 7.6 percentage points on fronthaul movements. This last effect could be explained by a rebound effect: higher expected LF on the returns lead carriers to accept shipments with lower fronthaul LF. Overall, we find that this technology has increased the tonne-kilometers transported of equipped trucks by 6.3% and their fuel efficiency by 5%.
This paper analyzes the impact of artificial intelligence (AI) on technological innovation through logic reasoning and empirical modeling. Based on the industrial robot data provided by the International Federation of Robotics (IFR) and the panel data of China's 14 manufacturing sectors from 2008 to 2017, this paper empirically analyzes the impact of AI on technological innovation. Our analysis shows that the mechanism of how AI affects technological innovation is that the former promotes technological innovation through accelerating knowledge creation and technology spillover, improving learning and absorptive capacities, while increasing R&D and talent investment. Our empirical results indicate that under the condition of controlling intensity of R&D investment, FDI, ownership structure, technical imitation, AI significantly promotes technological innovation. And the impact of AI on technological innovation experiences sector heterogeneity: AI has more significant impact on the technological innovation of low-tech sectors. The higher the level of AI, the greater its impact on technological innovation. Based on our established conclusions, we provide corresponding suggestions and recommendations for managerial decision-making.
We study the firm-level implications of robot adoption in France. Of 55,390 firms in our sample, 598 adopted robots between 2010 and 2015, but these firms accounted for 20 percent of manufacturing employment. Adopters experienced significant declines in labor shares, the share of production workers in employment, and increases in value added and productivity. They expand their overall employment as well. However, this expansion comes at the expense of competitors, leading to an overall negative association between adoption and employment. Robot adoption has a large impact on the labor share because adopters are larger and grow faster than their competitors.
Economic activities based on the invention, production, and distribution of artificial intelligence (AI) technologies have recently emerged worldwide. Yet, little is known about the innovative activities, location, and growth performance of AI innovators. This chapter aims to map and analyze the global innovative landscape of AI by exploring 155,000 patents identified as AI-related by means of text-mining techniques. It highlights the emergence and evolution of AI technologies and identifies AI hotspots across the world. It explores the scale and pervasiveness of AI activities across sectors and evaluates the economic performance of AI innovators using firm accounting information. Finally, it assesses recent trends in venture capital investments towards AI as financial support to promising AI startups.
Findings of this chapter reveal a tremendous increase in AI patenting activities since 2013 with a significant boom in 2015–2016. While most of AI patenting activities remain concentrated in the sectors of software programming and manufacturing of electronic equipment and machinery, there are clear signs of cross-fertilization towards (nontech) sectors. The market of AI patenting firms is very vibrant and characterized by a large increase of new and small players with economic performances above industry average. This trend is also reflected by the recent increase in venture capital towards AI startups.
Recent years have seen a reemergence of interest in artificial intelligence (AI) among both managers and academics. Driven by technological advances and public interest, AI is considered by some as an unprecedented revolutionary technology with the potential to transform humanity. But, at this stage, managers are left with little empirical advice on how to prepare and use AI in their firm’s operations. Based on case studies and the results of two global surveys among senior managers across industries, this article shows that AI is typically implemented and used with other advanced digital technologies in firms’ digital transformation projects. The digital transformation projects in which AI is deployed are mostly in support of firms’ existing businesses, thereby demystifying some of the transformative claims made about AI. This article then presents a framework for successfully implementing AI in the context of digital transformation, offering specific guidance in the areas of data, intelligence, being grounded, integrated, teaming, agility, and leadership.
With the process of digitalization now in full swing, many are wondering how the adoption of new technologies influences job creation and destruction. Much hinges upon the specific tasks that machines take on and how many new tasks are created through the adoption of new digital technologies. Some argue that most tasks that are at risk of automation are those performed by rather low- to medium-skilled employees, while most new tasks that emerge from the adoption of digital technologies complement high-skilled labor. We present evidence derived from representative survey data from Switzerland that is consistent with this view. Specifically, we find that increased investment in digitalization is associated with increased employment of high-skilled workers and reduced employment of low-skilled workers, with a slightly positive net effect. The main effects are almost entirely driven by firms that employ machine-based digital technologies, e.g. robots, 3D printing or the Internet of Things. We do not find any significant employment effects when non-machine-based digital technologies are considered, e.g. ERP, e-commerce or cooperation support systems.
Purpose
Big data analytics (BDA) guarantees that data may be analysed and categorised into useful information for businesses and transformed into big data related-knowledge and efficient decision-making processes, thereby improving performance. However, the management of the knowledge generated from the BDA as well as its integration and combination with firm knowledge have scarcely been investigated, despite an emergent need of a structured and integrated approach. The paper aims to discuss these issues.
Design/methodology/approach
Through an empirical analysis based on structural equation modelling with data collected from 88 Italian SMEs, the authors tested if BDA capabilities have a positive impact on firm performances, as well as the mediator effect of knowledge management (KM) on this relationship.
Findings
The findings of this paper show that firms that developed more BDA capabilities than others, both technological and managerial, increased their performances and that KM orientation plays a significant role in amplifying the effect of BDA capabilities.
Originality/value
BDA has the potential to change the way firms compete through better understanding, processing, and exploiting of huge amounts of data coming from different internal and external sources and processes. Some managerial and theoretical implications are proposed and discussed in light of the emergence of this new phenomenon.
The future of healthcare may change dramatically as entrepreneurs offer solutions that change how we prevent, diagnose, and cure health conditions, using artificial intelligence (AI). This article provides a timely and critical analysis of AI driven healthcare startups and identifies emerging business model archetypes that entrepreneurs from around the world have used to bring AI solutions to the marketplace. Through the secondary data and interviews with executives, we identify areas of value creation for the application of AI in healthcare and seven business model archetypes to propose a step by step approach to designing business models for AI healthcare startups.
This chapter defines the related terms for this topic and key issues surrounding the evolution of the science supporting the CI domain, together with an introduction of several of the tools and training practices developed to support the research in this area.World war two introduced many new technologies and expanded the engineering domain so rapidly that any impediments in a specific research topics were being abandoned in favor of more productive exploits. Over half a century has passed and we are still using the same fundamental computing architectures. Many disciplines have contributed to the development of agents, threads and component architecture. This chapter briefly discusses many of the key developments in CI and their relationship to this book.
This chapter reviews the empirical literature on the determination of firms’ and industries’ innovative activity and performance, highlighting the questions addressed, the approaches adopted, impediments to progress in the field, and research opportunities. We review the “neo-Schumpeterian” empirical literature that examines the effects of firm size and market concentration upon innovation, focusing on robust findings, questions of interpretation, and the identification of major gaps. We also consider the more modest literature that considers the effect on innovation of firm characteristics other than size. Finally, we review the literature that considers three classes of factors that affect interindustry variation in innovative activity and performance: demand, appropriability, and technological opportunity conditions.
The effect of firm size on the allocation of R&D effort between process and product innovation is examined. It is hypothesized that, relative to product innovations, process innovations are less saleable in disembodied form and spawn less growth. This implies that the returns to process R&D will depend more on the firm's output at the time it conducts its R&D than the returns to product R&D. Incorporating this distinction in a simple model, the authors derive and test predictions about how the fraction of R&D devoted to process innovation varies with firm size within industries. Copyright 1996 by MIT Press.
Regularities concerning how entry, exit, market structure, and innovation vary from the birth of technologically progressive industries through maturity are summarized. A model emphasizing differences in firm innovative capabilities and the importance of firm size in appropriating the returns from innovation is developed to explain the regularities. The model also explains regularities regarding the relationship within industries between firm size and firm innovative effort, innovative productivity, cost, and profitability. It predicts that over time firms devote more effort to process innovation but the number of firms and the rate and diversity of product innovation eventually wither. Copyright 1996 by American Economic Association.