Has Artificial Intelligence Promoted Manufacturing Servitization: Evidence from Chinese Enterprises

By inergency On Mar 19, 2024

1. Introduction

Manufacturing servitization is a crucial strategic initiative aimed at aligning with the ongoing international industrial transformation, propelling China’s industries towards the mid-to-high tiers of the global value chain, and facilitating the transition to high-quality economic development. The emergence of the intelligent era, epitomized by artificial intelligence, is ushering in a new phase of the industrial revolution, serving as a catalyst for the transformation towards manufacturing servitization. The 19th National Congress of the Communist Party of China emphasized the imperative to “accelerate the development of advanced manufacturing, promote the deep integration of the internet, big data, artificial intelligence, and the real economy”. Moreover, “Made in China 2025” refines the overarching direction by outlining the nation’s commitment to constructing a manufacturing powerhouse characterized by high-end sophistication, intelligence, environmental sustainability, and a service-oriented approach.

Furthermore, an undeniable fact is that in recent years, China’s economic development has shown a significant weakening of the comparative advantage derived from traditional low-cost resources and factor inputs. The development model relying on extensive factor inputs and export-driven growth is considered unsustainable. The impetus for China’s high-quality economic development now lies in the industrial revolution centered on digitization, networking, and intelligence. Simultaneously, in the post-pandemic era, developed countries such as the United States, Europe, and Japan have successively introduced “reindustrialization” strategies anchored in smart manufacturing to reshape their international leading position in high-end manufacturing through digitization and intelligence. Given this context, conducting a comprehensive exploration of the impacts and mechanisms of artificial intelligence on the level of manufacturing servitization is of great significance for China’s journey towards becoming a manufacturing powerhouse and achieving the transformation and upgrading of its manufacturing industry.

In the contemporary business landscape, the widespread adoption of artificial intelligence (AI), big data, and the Internet of Things (IoT) has become a defining characteristic, leading to significant technological advancements, open innovation, and collaboration [1]. This trend is particularly prominent in the manufacturing industry, often referred to as Industry 4.0 or the Fourth Industrial Revolution [2,3,4]. Consequently, research focusing on the application of AI in manufacturing enterprises offers valuable insights for our study [5]. For example, in a study conducted by Christian Stadlmann, the utilization of AI in web sales for companies operating in the advanced manufacturing sector was analyzed [6]. Several scholars have also examined the relationship between AI and servitization, exploring it from the perspective of dynamic capabilities [7] and within the context of Servitization 5.0 [8]. These scholars have conducted an analysis of the inverse U-shaped impact of AI-driven manufacturing intelligence on innovation performance [9], as well as an investigation into how various AI-based solutions support firms in co-creating value within the B2B (Business to Business) industrial market [10].

Furthermore, other researchers have analyzed how existing enterprises in the manufacturing industry can use artificial intelligence to achieve business model innovation in the industrial ecosystem [11,12]. These findings provide empirical insights into the intermediate development steps toward AI-driven business model innovation by leading manufacturers engaged in digital servitization. They also provide an in-depth characterization of AI capabilities and key principles for business model innovation as a means to assimilate AI into business practice [13]. Moreover, studies have also explored how the application of AI in manufacturing enterprises enhances resource efficiency, underscoring the importance of integrating sustainability with AI solutions [14].

In addition to these studies, two other literature sources also contribute significant implications to this research. The first category of literature focuses on the various roles of AI in the economic and social domains. As a new generation of information technology, the collaborative nature of AI features enhances input-output efficiency or total factor productivity, ultimately resulting in GDP growth [15]. Some scholars’ research indicates that AI can effectively address the challenges of aging populations [16], and AI and natural resource management contribute to economic growth [17]. The conclusions of another set of literature confirm the promoting effect of AI on productivity [18], primarily through reducing labor demand [19,20] and substituting cheaper capital for labor [21]. Some scholars also point out that AI is essentially a factor-expanding technology, which is beneficial for improving production efficiency [22].

Research on the impact of AI on employment has not yet reached a consensus. Some scholars’ research shows that the productivity improvement effect of AI leads to expanded production by firms, thereby increasing employment opportunities [23]. Furthermore, while AI displaces employment in certain industries, it also creates new types of jobs through “creation effects,” thereby causing changes in the overall employment structure [24]. The negative impact of AI on employment mainly manifests in the polarization of employment resulting from AI shocks [25,26]. The research of the vast majority of scholars shows that AI significantly reduces the share of low-skilled workers in employment [27], and this employment structure leads to an expanding income gap between low-skilled and high-skilled workers [28].

Another category of literature analyzes the driving mechanisms and economic effects of manufacturing servitization. Manufacturing servitization creates new value by integrating products and services [29]. Thus, manufacturing servitization significantly improves firms’ innovation performance [30] and is an effective approach for reshaping their competitive advantages and achieving sustainable development [31]. Manufacturing servitization facilitates the strengthening of cooperation in the global value chain division of labor and the embeddedness of various clusters within the value chain network [32], thereby significantly enhancing firms’ position in the global value chain [33,34,35]. From this perspective, the higher the division of labor position of manufacturing in the global value chain, the greater the productivity effect of servitization [36]. Regarding the export effects of manufacturing servitization, the research of the vast majority of scholars indicates that the transformation of manufacturing inputs into services accelerates the process of firms’ export upgrading from “quantity-oriented” to “quality-oriented” [37], but this effect exhibits industry heterogeneity [38]. Furthermore, some literature examines the impact of manufacturing servitization on firm performance, yielding three different viewpoints: promotion [39,40], inhibition [41], and nonlinear relationships [42,43].

On the other hand, independent innovation is an important driving force for the servitization of the manufacturing industry [44]. This is because the improvement in product innovation capabilities enables manufacturers to provide customized services to customers through product design enhancement and the manufacturing of new products, thereby promoting the transformation towards a service-oriented development [29]. Some literature also examines the positive impact of internet technologies [45] and digital finance [46] on the servitization of the manufacturing industry. Scholars have also analyzed the impact of research and development personnel ratio, input intensity, and the proportion of clean energy from an ecological perspective on the servitization of the manufacturing industry [47]. Furthermore, some scholars have studied the role of manufacturing servitization in reducing firms’ emission intensity [48]. From the perspective of international industrial evolution trends and development patterns, the transformation and upgrading of the manufacturing industry rely on the support of productive service industries [49].

Overall, existing literature on the economic effects of manufacturing servitization is relatively abundant, but there is relatively little research on how to achieve manufacturing servitization. Moreover, existing literature only discusses this issue from the perspective of the development of productive service industries, with few studies incorporating artificial intelligence and manufacturing servitization into a unified analytical framework to directly examine the impact of AI development on manufacturing servitization. Therefore, this article seeks to empirically study the impact of artificial intelligence on the manufacturing industry, based on matched data from the OECD-ICIOT (Organization for Economic Co-operation and Development, Intercountry Input-Output Tables) industry data, the China Industrial Enterprise Database, and the Customs Trade Database. The aim is to provide valuable insights on how to leverage the positive role of artificial intelligence in the manufacturing sector. The research findings indicate that artificial intelligence significantly and robustly enhances the level of servitization in manufacturing enterprises. This effect is primarily achieved through two channels: improving enterprise total factor productivity and optimizing the labor skill structure. Furthermore, when distinguishing between different ways of transforming manufacturing services, this study reveals that artificial intelligence plays a significant facilitating role in the embedded service transformation, while its impact on the blended service transformation is not evident.

This paper contributes to three main aspects in comparison to previous research. Firstly, it accurately measures the level of manufacturing servitization at the enterprise level by distinguishing between domestic and foreign factor inputs. This enables the provision of micro-level evidence on how artificial intelligence influences manufacturing servitization. Secondly, it extends the analysis framework of domestic value-added in exports proposed by Kee and Tang [50] to the field of manufacturing servitization. The paper constructs a theoretical framework that incorporates the constraints of artificial intelligence inputs and labor skill inputs. Using this framework, it explores the theoretical mechanisms through which artificial intelligence affects manufacturing servitization within a general equilibrium framework, considering the impact on enterprise total factor productivity and the optimization of labor skill structure. Thirdly, the paper further distinguishes manufacturing servitization into embedded services and hybrid services, providing clarification on the differentiated effects of different types of service transformations. Fourthly, it enriches the positive role of artificial intelligence in the manufacturing sector, providing a good inspiration for China to better integrate artificial intelligence with the real economy, build manufacturing power, and promote the development of the intelligent era.

The subsequent structure of this paper is as follows: Section 2 provides a theoretical analysis and presents the hypotheses of this study. The complete model derivation process can be found in Appendix A. Section 3 describes the econometric model, data sources, and relevant indicator explanations. Section 4 presents the empirical results analysis and discussion. Section 5 further examines the differential effects of artificial intelligence on the transformation of embedded services and hybrid services. Finally, the main research conclusions and policy implications are presented.

5. Further Discussion: Embedded Service Transformation and Blended Service Transformation

Embedded service transformation primarily involves integrating resources of both products and services, thereby moving products from the lower end to the higher end of the value chain. As a result, the main products involved in embedded transformation exhibit a strategic matching relationship with the original products throughout the value chain. Conversely, blended service transformation primarily extends into higher-value service sectors to explore new sources of profit growth.

To investigate the distinct effects of artificial intelligence on these two transformation modes, this study employs a fuzzy matching process to combine the Guo-Qian An CSMAR listed company database for the period 2005–2015 with the previously mentioned industrial-enterprise-customs merged data. The study filters out companies that reported service revenue in their annual reports for a minimum of two years, yielding a sample of 97 companies comprising 276 observations.

The corresponding econometric model is formulated as follows:

${S e r v i c e}_{f i t} = β_{0} + {β_{1} A I}_{i t} + β_{2} {c o n t r o l}_{f i t} + δ_{f} + η_{t} + ε_{f i t}$

(5)

In the equation, the variable “Service” represents the degree of manufacturing servitization, encompassing two distinct types: embedded and blended servitization. The level of embedded servitization (Embedded) is quantified as the proportion of revenue generated from embedded services, such as product distribution, product installation, after-sales maintenance, testing, recycling, remote monitoring, engineering consulting, energy efficiency, logistics consulting, IT solutions, etc., to the total operating revenue. Similarly, the level of blended servitization (Mixed) is assessed based on the percentage of revenue derived from blended services, such as futures brokerage, engineering services, property leasing, property management, department stores, trade, catering, and tourism, etc., out of the total operating revenue. The explanations for other variables remain consistent with the previous descriptions.

The regression results presented in Table 5 demonstrate a significant positive impact of artificial intelligence on embedded service transformation, whereas its influence on blended service transformation remains inconclusive. This suggests that artificial intelligence frequently extends the value chain of enterprises by incorporating activities like research and development, after-sales support, and technical services. As a result, it facilitates the advancement of their manufacturing servitization level. Similarly, motivated by artificial intelligence technology, enterprises exhibit a relatively modest inclination to reconfigure resource elements and expand their business scope. Additionally, this outcome underscores the significance of achieving coopetition (cooperation and competition) throughout the value chain, as it enables the mastery of core capabilities and the sharing of global benefits. Moreover, it further emphasizes the pivotal role played by artificial intelligence in the ongoing wave of industrial transformation and upgrading.

6. Conclusions and Policy Implications

As information technology advances rapidly, manufacturing enterprises are facing an urgent need to transition from being product suppliers to becoming comprehensive solution providers. The emergence of artificial intelligence technology further accelerates this transformation. This paper utilizes the 2018 version of OECD-ICIOT industry data and micro-matched data from customs and industrial enterprise databases to calculate the level of manufacturing servitization at the enterprise level from 2005 to 2015. Subsequently, it investigates the impacts of artificial intelligence on manufacturing servitization in China. The research findings indicate that artificial intelligence significantly promotes manufacturing servitization, primarily through mechanisms such as improving enterprise TFP and optimizing labor structure. Additionally, the study reveals that artificial intelligence has a significant promoting effect on the transformation of embedded services, while its impact on blended service transformation is not evident.

The present study holds significant theoretical and practical implications. On the theoretical front, this study extends the enterprise domestic value-added analysis framework proposed by Kee and Tang [50] to the realm of manufacturing servitization. It establishes a theoretical analytical framework that reflects the constraints of artificial intelligence inputs and labor skill inputs. Based on this framework, the study explores the theoretical mechanisms through which artificial intelligence influences the servitization of the manufacturing industry by impacting enterprise total factor productivity and optimizing labor skill structures within a general equilibrium framework.

On the practical level, this study provides accurate measurements of the level of manufacturing servitization at the firm level, taking into account the differentiation between domestic and foreign sources of factor inputs. Consequently, it offers micro-level evidence of how artificial intelligence influences manufacturing servitization. Building upon this evidence, the study further distinguishes between embedded services and mixed services within the realm of manufacturing servitization, thereby clarifying the differentiated effects of different types of servitization transformations. This serves as valuable guidance for manufacturing enterprises to fully leverage artificial intelligence as a cutting-edge technology for achieving servitization transformation and making appropriate adjustments based on specific types of servitization transformations.

The conclusions drawn from this research have important policy implications. Firstly, it is crucial to provide strong support for the development of the artificial intelligence industry by strengthening the infrastructure and innovation platform, thereby promoting the deep integration of artificial intelligence with the manufacturing sector and other real economy sectors. This integration facilitates the transformation and upgrading of manufacturing enterprises. Secondly, increasing investment in worker education and skills training is essential to enable low-skilled workers to enhance their capabilities and adapt to the new landscape of manufacturing servitization, thereby reducing the potential employment impact resulting from the advancement of artificial intelligence in the manufacturing sector. Thirdly, actively leveraging artificial intelligence technology to promote intelligent matching and efficient collaboration between services and production factors can accelerate the pace of manufacturing servitization. Fourthly, expanding the positive impact of artificial intelligence is necessary to ensure its full potential in relatively underdeveloped regions, labor-intensive industries, private enterprises, processing trade enterprises, and hybrid-service-oriented businesses, fostering balanced development.

Finally, the present research in this paper still has some limitations. For example, since the latest customs data has not been disclosed in recent years, public data is available only up to 2015. At the same time, the measurement of the level of artificial intelligence development has been limited to industry-level analysis, failing to capture more granular results. Therefore, in future research, we will continuously update the data obtained and attempt to measure the level of artificial intelligence development at the enterprise level, aiming to conduct more in-depth and timely studies.