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Considering rapid digitalization development, this study examines the impacts of digital technology innovation on social responsibility in platform enterprises.

The study applies the event study method and cross-sectional regression analysis, taking 168 digital technology innovations for social responsibility issued by 88 listed platform enterprises from 2011 to 2022 to study the impact of digital technology innovations for social responsibility announcements of different announcement content and platform attributes on the stock market value of platform enterprises.

The results show that, first, the positive stock market reaction is produced on the same day as the digital technology innovation announcement. Second, the announcement of the platform’s public social responsibility and the announcement of co-innovation and radical innovation bring more positive stock market reactions. In addition, the announcements mentioned above issued by trading platforms bring more positive stock market reactions. Finally, the social responsibility attribution characteristics of the announcement did not have a significant differentiated impact on the stock market reaction.

Most scholars have studied digital technology innovation for social responsibility through modeling rather than second-hand data to empirically examine. This study uses second-hand data with the instrumental stakeholder theory to provide a new research perspective on platform social responsibility. In addition, in order to explore the different impacts of digital technology innovation on social responsibility, this study has classified digital technology innovation for social responsibility according to its social responsibility and digital technology innovation characteristics.

This study aims to investigate the critical success factors of digital transformation in the construction industry and identify whether the respondents' profiles influence their perceptions of critical success factors for digital transformation.

To achieve the objectives, a literature review was first conducted based on technology-organization-environment (TOE) framework. Then a questionnaire survey was carried out. A total of 86 people were surveyed in this study, mainly from the construction industry. At the level of data processing, SPSS was used for analysis. Among the main tests used were the Shapiro–Wilk test, reliability analysis, mean rank analysis, Kruskal–Wallis test and Mann–Whitney U test.

The study identified 15 critical success factors of digital transformation and found the three most important factors of digital transformation. Furthermore, respondents with different years of experience, enterprises with different sizes and different years made no difference in the perception of factors. Respondents' different occupations and types of enterprises created a bias in the perception of factors for digital transformation.

Firstly, the small sample size of the questionnaire limits the reference value of data analysis for certain groups. In addition, this study focuses broadly on construction enterprises without specifically examining different types of enterprises, thus lacking depth in its findings.

This study establishes a connection between TOE theory and the construction industry through an extensive literature review, identifying relevant factors and providing a reference for future research.

The study's results would enrich the research on digital transformation in the construction industry and provide a reference for the digital transformation of construction enterprises.

This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.

Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.

It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.

Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.

Driven by the dual-carbon target and the widespread digital transformation, leveraging digital technology (DT) to facilitate sustainable, green and high-quality development in heavy-polluting industries has emerged as a pivotal and timely research focus. However, existing studies diverge in their perspectives on whether DT’s impact on green innovation is synergistic or leads to a crowding-out effect. In pursuit of optimizing the synergy between DT and green innovation, this paper aims to investigate the mechanisms that can be harnessed to render DT a more constructive force in advancing green innovation.

Drawing from the theoretical framework of resource orchestration, the authors offer a comprehensive elucidation of how DT intricately influences the green innovation efficiency of enterprises. Given the intricate interplay within the synergistic relationship between DT and green innovation, the authors use the fuzzy-set qualitative comparative analysis method to explore diverse configurations of antecedent conditions leading to optimal solutions. This approach transcends conventional linear thinking to provide a more nuanced understanding of the complex dynamics involved.

The findings reveal that antecedent configurations fostering high green innovation efficiency actually differ across various stages. First, there are three distinct configuration patterns that can enhance the green technology research and development (R&D) efficiency of enterprises, namely, digitally driven resource integration (RI), digitally driven resource synergy (RSy) and high resource orchestration capability. Then, the authors also identify three configuration patterns that can bolster the high green achievement transfer efficiency of enterprises, including a digitally optimized resource portfolio, digitally driven RSy and efficient RI. The findings not only contribute to advancing the resource orchestration theory in the digital ecosystem but also provide empirical evidence and practical insights to support the sustainable development of green innovation.

The findings can offer valuable insights for enterprise managers, providing decision-making guidance on effectively harnessing the innovation-driven value of internal and external resources through resource restructuring, bundling and leveraging, whether with or without the support of DT.

The research findings contribute to heavy-polluting enterprises addressing the paradoxical tensions between digital transformation and resource constraints under environmental regulatory pressures. It aims to facilitate the simultaneous achievement of environmental and commercial success by enhancing their green innovation capabilities, ultimately leading to sustainability across profit and the environment.

Compared with previous literature, this research introduces a distinctive theoretical perspective, the resource orchestration view, to shed light on the paradoxical relationship on resource-occupancy between DT application and green innovation. It unveils the “black box” of how digitalization impacts green innovation efficiency from a more dynamic resource-based perspective. While most studies regard green innovation activities as a whole, this study delves into the impact of digitalization on green innovation within the distinct realms of green technology R&D and green achievement transfer, taking into account a two-stage value chain perspective. Finally, in contrast to previous literature that predominantly analyzes influence mechanisms through linear impact, the authors use configuration analysis to intricately unravel the complex influences arising from various combinatorial relationships of digitalization and resource orchestration behaviors on green innovation efficiency.

3D printing has been warmly welcomed by clothing enterprises for its customization capacity in recent years. However, such clothing enterprises have to face the digital transformation challenges brought by 3D printing. Since the business model is a competitive weapon for modern enterprises, there is a research gap between business model innovation and digital transformation challenges for 3D-printing garment enterprises. The aim of the paper is to innovate a new business model for 3D-printing garment enterprises in digital transformation.

A business model innovation canvas (BMIC), a new method for business model innovation, is used to innovate a new 3D-printing clothing enterprises business model in the context of digital transformation. The business model canvas (BMC) method is adopted to illustrate the new business model. The business model ecosystem is used to design the operating architecture and mechanism of the new business model.

First, 3D-printing clothing enterprises are facing digital transformation, and they urgently need to innovate new business models. Second, mass customization and distributed manufacturing are important ways of solving the business model problems faced by 3D-printing clothing enterprises in the process of digital transformation. Third, BMIC has proven to be an effective tool for business model innovation.

The new mass deep customization-distributed manufacturing (MDC-DM) business model is universal. As such, it can provide an important theoretical reference for other scholars to study similar problems. The digital transformation background is taken into account in the process of business model innovation. Therefore, this is the first hybrid research that has been focused on 3D printing, garment enterprises, digital transformation and business model innovation. On the other hand, business model innovation is a type of exploratory research, which means that the MDC-DM business model’s application effect cannot be immediately observed and requires further verification in the future.

The new business model MDC-DM is not only applicable to 3D-printing garment enterprises but also to some other enterprises that are either using or will use 3D printing to enhance their core competitiveness.

A new business model, MDC-DM, is created through BMIC, which allows 3D-printing garment enterprises to meet the challenges of digital transformation. In addition, the original canvas of the MDC-DM business model is designed using BMC. Moreover, the ecosystem of the MDC-DM business model is constructed, and its operation mechanisms are comprehensively designed.

The purpose of this paper is to explore how, in a context of economic, political, social and environmental transitions, SMart, a cultural and artistic social enterprise (CASE), has developed a relevant cooperative model to contribute to mitigate the structural labour precariousness of artists and creators.

The research design includes a mixed-method approach that combines qualitative and quantitative methodologies with emphasis on the former. Namely, the organisation is SMartbe and its replication across nine European countries was studied as a revelatory case study. Five main types of techniques were deployed in the course of this research, including desk review, direct (participant) observation, interviews, focus group and questionnaire (Likert-scale survey).

CASEs constitute a specific institutional arrangement that offers innovative labour arrangements for cultural workers and artists to fight against precariousness. Social enterprises are embedded in the social and solidarity economy and stand at the crossroads of markets, civil society and the public, which places them in a critical position: depending on the logic, actors and contexts at play, social enterprises can ensure to varying degrees the general interest through their social mission, their sustainability via the real participation of all their stakeholders and the carrying out of economic activities that are fully consistent with their mission.

It is within this type of sustainability and participation that transformative social innovation can emerge within CASEs. Their potential to contribute to transformative social innovation is based on its four objectives: cultural (to imagine human, participative and sustainable alternatives); social (to achieve a social –including the environment – mission and join the ecosocial transition); participatory (empowering and impacting the public sphere); and economic (being financially sustainable and fair).

The purpose of this study is to investigate whether repeated alliances between two members of a patent pool boost enterprise innovation. Furthermore, this paper intends to determine whether the innovation performance becomes higher or lower based on the partnership characteristics.

In this empirical study, hierarchical regression is used to analyze the longitudinal data obtained from 12 patent pools managed by MPEG LA during a time period ranging from 2006 to 2018. The members of patent pools comprise research institutions, firms and universities. Research analyses are performed based on a sample of 68,400 member pairs who had established repeated alliances. The information regarding such pairs is gathered from public databases.

Repeated alliances positively correlate with enterprise innovation performance in patent pools; this performance is higher when the two enterprises have exploratory collaborations. Conversely, the performance is lower when the partners have a similar technology base and are engaged in a technological competition (competitive learning and patent litigation). Moreover, the performance is lower when one partner demonstrates higher network centrality and richer structural holes than the other partner.

Patent pools play an instrumental role in eliminating patent-licensing barriers, thereby allowing mutual acquisition of complementary technologies, and cooperatively strengthening technology development. From the perspective of theories of coopetition, knowledge management and social network, this study explores the impact of patent pools on enterprise innovation performance and ascertains the moderating roles of technology coopetition, technology similarity and network position, thereby expanding the scope of innovation effect in the context of patent pools.

Policymakers are developing government-level pandemic response strategies (GPRS) to assist architecture, engineering and construction (AEC) enterprises. However, the effectiveness of the GPRS has not been assessed. Therefore, this study aims to investigate the interrelationships between GPRS and AEC enterprises. To achieve that aim, the study objectives are to compare GPRS effectiveness between small-medium and large AEC enterprises, develop groupings to categorize interrelated GPRS and evaluate the effectiveness of the GPRS and interrelated constructs.

A systematic literature review and semi-structured interviews with 40 AEC industry professionals were carried out, generating 22 GPRS. Then, questionnaire survey data was collected among AEC professionals. In total, 114 valid survey answers were received and analyzed using the Kruskal–Wallis H test, normalized mean analysis, factor analysis and fuzzy synthetic evaluation.

Small-medium enterprises have four distinct critical GPRS: “form a special task force to provide support in maneuvering COVID-19,” “provide infrastructure investment budgets to local governments,” “develop employee assistance programs that fit all types of working groups” and “diversify existing supply chain.” Large enterprises have two distinct critical GPRS: “provide help in digitalizing existing construction projects” and “mandate COVID-19 as force majeure.” Eighteen GPRS can be categorized into the following five constructs: “market stability and financial aid,” “enterprise capability management,” “supply chain improvement,” “law and policy resources” and “information and workforce management.” The former two constructs are more effective than other GPRS constructs.

This is the first paper that evaluates the effectiveness of GPRS for AEC enterprises, providing new evidence to policymakers for well-informed decision-making in developing pandemic response strategies.

The paper aims to explore the roles and impact of design in incubating and accelerating social enterprises. It aims to understand design’s influence on social enterprise ecosystems and in improving outcomes for social enterprises.

The study used an exploratory, qualitative approach, using case studies and interviews. The comparative case-study methodology was applied to evaluate the influence of design on the development of social enterprises in the UK and South Korea and identify critical issues in their utilisation of design. Empirical data included: in-depth case studies of design utilisation practices (UK = 6; South Korea = 15) and design applications (UK = 2; South Korea = 2) for the growth of social enterprise and its ecosystem; 27 social enterprise/design experts (UK = 17; South Korea = 10); and 22 social enterprises (UK = 12; South Korea = 10). Content and thematic analysis were used to synthesise the findings.

Findings demonstrate the differing influences of design on social enterprise, from improving products/services and business models to enhancing social enterprise ecosystem support and networks. Future directions are suggested for applying design for social enterprise growth, business stage development and systematising interactions between the social enterprise and design sectors.

The research is based on case studies from only two countries. Further, the adoption of working definitions of social enterprise in the countries may result in the research underestimating the heterogeneity of social enterprise.

The findings contribute to optimising efficient ecosystem development to improve social enterprise competitiveness and innovation.

This paper establishes a research foundation on design for social enterprise, offering theoretical and practical insights into its impact on growth.

Previous studies have reached inconsistent conclusions on foreign direct investment (FDI) technology spillovers and corporate innovation. The main purpose of this paper is to explore the technological spillover effects of FDI from the microperspective of firm linkages induced by geographic distance. Further analysis is conducted on the impact and mechanism of this spillover on the innovation quality of Chinese enterprises. The conclusions drawn from this paper can guide Chinese enterprises' foreign capital utilization and innovation strategy choices.

Using the data of China's A-share listed companies from 2009 to 2019, this paper explores the role of FDI technology spillover in enterprise innovation quality through a two-way fixed-effect model. The robustness of the results is proven by substituting variables, adding industry fixed effects and excluding high-profit groups, and further using the two-stage least squares (2SLS) method to alleviate the empirical endogeneity problem.

These findings indicate that FDI technology spillover based on geographic proximity has a positive impact on the innovation quality of Chinese enterprises. However, there are different impacts for different types of enterprises. FDI technology spillover has a positive impact on the innovation quality of non-state-owned enterprises (non-SOEs) and small- and medium-sized enterprises (SMEs), while it has no effect on state-owned enterprises (SOEs) and large enterprises. The authors also find that the degree of financing constraints and R&D investment are important transmission mechanisms between FDI technology spillover and enterprise innovation quality.

This study ignores industry characteristics when considering foreign enterprises around Chinese enterprises. In fact, technology spillover effects differ across industries. When the authors matched microdata to regions, only the provincial level was considered. Therefore, there is still room for further research. In future research, the authors should consider industry characteristics and group foreign enterprises and Chinese enterprises in the same industry and in different industries to explore industry differences in technology spillover. In addition, when matching corporate data to regions, the authors can match to the city level and draw city-level conclusions.

This study is different from previous studies that focus on the quantity of enterprise innovation or innovation output. The authors focus on the role of technological spillovers in the quality of Chinese enterprise innovation, enriching research in the field of enterprise innovation quality. In addition, the current FDI technology spillover indicators are technically difficult to measure at the micro level. The authors draw inspiration from the theory of the geographical structure of financial supply and combine the creation methods of macro and micro indicators in existing articles in other fields. The authors ingeniously construct a new FDI technical spillover indicator. This indicator combines the commonly used regional FDI technology spillover with the geographic proximity of enterprises at the microlevel by constructing an interaction term between the two. This indicator not only alleviates the endogeneity problem to a certain extent but also has implications for future research in the field of FDI technology spillovers at the micro level.

(1) FDI technology spillovers are an effective way to improve the innovation quality of local enterprises, especially for non-SOEs and SMEs. Therefore, The authors suggest that in the context of dual circulation, the Chinese government should continue to open wider to the outside world and encourage foreign enterprises to invest in China. (2) In future development, managers of SOEs and large enterprises should create an innovation incentive mechanism. Moreover, they should change their vertical management structure and make full use of their policy advantages and budget advantages to increase innovation activities. In the process of acquiring technology spillovers, enterprises need to solve their own financing constraints.

First, this study solves a technical problem. It is technically difficult to measure the current FDI technical spillover indicators at the micro level. This study innovatively constructs a new FDI technology spillover indicator that combines regional FDI technology spillovers with the microperspective of the geographical proximity of enterprises. This approach not only alleviates certain endogeneity problems in the empirical evidence but also enriches relevant research in the field of technology spillover. In addition, this study focuses on the impact and mechanism of this spillover, which addresses the current research gap among previous studies that mainly focus on innovation quantity and ignore innovation quality.