Search results

This paper explores the orchestration of digital innovation in Industry 4.0 organisations.

The study applies the activity theory to explorative multiple case studies. Observations of innovation activities in five business cases take place at two large international organisations.

The results underline five logics of action that drive digital innovation: (1) digital transformation, (2) technology translation, (3) catalyst agents, (4) digital thread and (5) empowerment. Further, the case study organisations highlight the importance of developing a sustainable culture capable of continuously adopting new technologies, processes and infrastructure that will allow the management of digital innovations.

The study empirically shows the motivations and challenges in orchestrating digital innovation in Industry 4.0 organisations.

The purpose of this paper is to investigate the impact of the COVID-19 pandemic on company innovation, specifically centering on the quantity and quality of innovation. The paper aims to provide a comprehensive understanding of whether the epidemic inhibits innovation and the role of digital transformation in mitigating this negative impact.

The paper uses a quasi-experimental study of the COVID-19 pandemic and constructs a differential model to analyze the relationship between the epidemic and firm innovation in three dimensions: total, quantity and quality. The paper also uses a difference-in-difference-in-differences model to test whether digital transformation of firms mitigates the negative impact of the epidemic and its mechanism of action.

The results show that COVID-19 significantly reduced the overall level of firm innovation, primarily in terms of quantity rather than quality. Furthermore, this study finds that digital transformation plays a pivotal role in mitigating the pandemic’s adverse impact on innovation. By addressing financing constraints and countering demand insufficiency, digital transformation acts as a catalyst for preserving and fostering innovation during and after the pandemic.

This study extends the current research on the pandemic’s impact on firm innovation at the micro level. It offers valuable insights into strategies for fostering digital transformation among Chinese enterprises in the post-pandemic era.

Digital innovation requires organizations to reconfigure their information technology infrastructure (ITI) to cultivate creativity and implement fast experimentation. This research inquiries into ITI generativity, an emerging concept demoting a critical ITI capability for organizational digital innovation. More specifically, it conceptualizes ITI generativity across two dimensions—namely, systems and applications infrastructure (SAI) generativity and data analytics infrastructure (DAI) generativity—and examines their respective social and technical antecedents and their impact on digital innovation.

This research formulates a theoretical model to investigate the social and technical antecedents along with innovation outcomes of ITI generativity. To test this model and its associated hypotheses, a survey was administered to IT professionals possessing knowledge of their organization's IT architecture and digital innovation performance. The dataset, comprising responses from 140 organizations, was analyzed using the partial least squares technique.

Results reveal that both dimensions of ITI generativity contribute to digital innovation performance, with the effect of DAI generativity being more pronounced. In addition, SAI and DAI generativities are driven by social and technical factors within an organization. More specifically, SAI generativity is positively associated with the usage of a digital application services platform and IT human resources, whereas DAI generativity is positively linked to the usage of a data analytics services platform, data analytics services usability and data analytics human resources.

This research contributes to the literature on digital innovation by introducing ITI generativity as a crucial ITI capability and deciphering its role in digital innovation. It also offers useful insights and guidance for practitioners on how to build ITIs to achieve better digital innovation performance.

Food sustainability is a world-acknowledged issue that requires urgent integrated solutions at multi-levels. This study aims to explore how food firms can improve their sustainability performance through digital traceability practices, considering the mediating effect of sustainability-oriented innovation (SOI) and the moderating effect of supply chain learning (SCL) for the food supply chain therein.

Hierarchical regression with a moderated mediation model is used to test the proposed hypotheses with a sample of 359 food firms from four provinces in China.

Digital traceability has a significant positive impact on the three pillars of sustainability performances among food firms. SOI (product innovation, process innovation and organisational innovation) mediates the relationship between digital traceability and sustainability performance. SCL plays moderating roles in the linkage between digital traceability and both product and process innovation, respectively.

This paper contributes as one of the first studies to develop digital traceability practices and their sustainability-related improvements for Chinese food firms; it extends studies on supply chain traceability to a typical emerging market. This finding can support food sustainability practice in terms of where and how to invest in sustainability innovation and how to improve economic, environmental and social performance.

Digital transformation has gained particular interest among academics and policymakers in recent years. However, the empirical quantification of digital transformation stages and their impact on innovation in small and medium-sized enterprises (SMEs) remains understudied. Therefore, this study aims to investigate the impact of digital transformation stages on a differentiated measurement of innovation performance in SMEs.

The authors propose a simplified one-dimensional digital maturity path to estimate the stages of digital transformation in SMEs. The authors validate their approach with a cluster analysis and perform an ordered logistic regression to estimate the impact of digital transformation stages on SMEs' innovation performance.

The authors' results show that digital transformation in general has a positive impact on SMEs' innovation performance. More precisely, the authors find that the early stage of digital transformation has a detrimental effect on innovation performance, while significant and positive effects can be expected from the experimental stage onward. Furthermore, the advanced stage of digital transformation significantly increases the probability of producing radical innovations.

This study contributes to the ongoing discussion about the relationship between digital transformation and innovation in SMEs by presenting an approach to quantify digital transformation stages in SMEs. Additionally, this study provides new insights into the specific dynamics of the relationship between different stages of digital transformation and their impact on a differentiated measurement of innovation performance, including technological, non-technological and radical innovation.

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.

Research has shown that business model innovation can facilitate most ventures to innovate and remain competitive, yet there has been limited work on how digital leadership capabilities influence business model innovation. Building on the dynamic capabilities view, we address this gap by linking digital leadership capabilities with business model innovation via managerial decision-making through provision of grants received by new ventures.

The study is cross-sectional research. Data have been collected utilizing purposive sampling from 313 founding members of new ventures in high-velocity markets, i.e. from Pakistan. SPSS has been used to conduct the moderated mediation analysis.

Digital leadership capabilities foster the business model innovation of the new ventures because they enable new ventures to capitalize on digital technologies and create new ways of generating value for the customers and themselves. Moreover, managerial decision-making mediates digital leadership capabilities and business model innovation relationship, whereas, grants moderate the indirect positive effect of digital leadership capabilities on business model innovation via managerial decision-making. The study generates initial evidence on the impact of digital leadership capabilities on business model innovation via managerial decision-making for new ventures. We advance knowledge on new ventures’ business model innovation by deep-diving into dynamic capabilities view and emphasizing digital leadership capabilities as a significant driver for business model innovation.

With the help of dynamic capabilities theory, this study analyzes how new ventures make use of digital leadership capabilities to promote business model innovation.

Enterprises in the digital context attach importance to the participation of heterogeneous subjects in co-creation activities. As the core source of organizational innovation, employees are bound to assume responsibilities or provide role value in the process of digital innovation. While employee-driven digital innovation frameworks are being explored, together with research on employee innovation behavior mostly focusing on the “net effect” of single-level variables on outcomes.

Following the interaction logic of employee level and organizational level, this study applies ability, motivation and opportunity (AMO) theory and fuzzy set qualitative comparative analysis to explore the influence of various digitization related factors' conditional configuration on employees' digital innovation behavior.

The results indicate that type of “self-motivation with multiple supports”, “organization-driven with digital oriented” and “digital-driven with ambidextrous complementary” constitute the configuration that drives high digital innovation behavior, while type of “cognitive deficit with environmental slack” and “fuzzy motivation with opportunity misapplication” constitute the configuration that drives nonhigh digital innovation behavior.

This conclusion enriches the theoretical research on digital innovation that regards individuals as action potentials and provides important guidance for how to cultivate and activate employees' digital innovation behaviors in management practice.

The aim of this study is to (1) construct a standard framework for assessing the capability of bioenergy enterprises' digital green innovation partners; (2) quantify the choice of partners for digital green innovation by bioenergy enterprises; (3) propose based on a dual combination empowerment niche digital green innovation field model.

Fuzzy set theory is combined into field theory to investigate resource complementarity. The successful application of the model to a real case illustrates how the model can be used to address the problem of digital green innovation partner selection. Finally, the standard framework and digital green innovation field model can be applied to the practical partner selection of bioenergy enterprises.

Digital green innovation technology of superposition of complementarity, mutual trust and resources makes the digital green innovation knowledge from partners to biofuels in the enterprise. The index rating system included eight target layers: digital technology innovation level, bioenergy technology innovation level, bioenergy green level, aggregated digital green innovation resource level, bioenergy technology market development ability, co-operation mutual trust and cooperation aggregation degree.

This study helps to (1) construct the evaluation standard framework of digital green innovation capability based on the dual combination empowerment theory; (2) develop a new digital green innovation domain model for bioenergy enterprises to select digital green innovation partners; (3) assist bioenergy enterprises in implementing digital green innovation practices.

Despite the wide research and discussion on international performance, innovation and digital resilience dimensions of enterprises, the investigation and understanding of their interrelations seem to be limited. The purpose of this study is to identify the influential factors affecting the mentioned dimensions, determine the causal relationships among these identified factors and finally evaluate their importance in an aggregated framework from the viewpoint of small and medium-sized enterprises (SMEs).

A hybrid methodology is used to achieve the objectives. First, the main factors of international performance, innovation and digital resilience are extracted by an in-depth review of the literature. These factors are then screened by expert opinions to localize them in accordance with the conditions of an emerging economy. Finally, the relationship and the importance of the factors are determined using an uncertain multi-criteria decision-making (MCDM) approach.

The findings reveal that there is a correlation between digital resilience and innovation, and both factors have an impact on the international performance of SMEs. The cause-or-effect nature of the factors belonging to each dimension is also determined. Among the effect factors, business model innovation (BMI), agility, product and organizational innovation are known as the most important factors. International knowledge, personal drivers and digital transformation are also determined to be the most important cause factors.

This study extends the literature both in methodological and practical directions. Practically, the study aggregates the factors in the mentioned dimensions and provides insights into their cause-and-effect interrelations. Methodologically, the study proposes an uncertain MCDM approach that has been rarely used in previous studies in this field.