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This paper aims to suggest a procedure for successfully transforming a firm’s innovation processes in a systematic way.

This is a conceptual paper, which draws on prior academic and practitioner papers.

Changes in a firm’s environment, such as new technological trends or customer needs, regularly call for the dynamic renewal of a firm’s innovation processes. Nonetheless, most firms proceed in a surprisingly unsystematic way if they transform their innovation processes. This approach contrasts with the systematic innovation processes that many firms have established to manage their product development from initial idea to final market launch.

To overcome this discrepancy, this paper distinguishes reconfiguration and realignment challenges in the transformation of a firm’s innovation processes. These different activities are illustrated with the example of transforming firms’ innovation processes towards open innovation. Furthermore, a five-step procedure is suggested to ease implementation. On this basis, implications for managers are discussed with respect to proficiently adapting their firms’ innovation processes over time.

In a session of the Strategos Innovation Academy, participants considered how a number of core management processes – for example, strategic planning, capital budgeting, performance assessment and product and process development – inhibit innovation. Working in groups, the participants identified problems with existing practices and then suggested a number of ways to make the process less toxic to innovation. Today’s strategic‐planning processes rarely emphasize radical innovation – the new business concepts and operational models that are necessary to keep corporations at the head of the pack – either implicitly or explicitly. Another failure that participants identified is the linkage between strategy planning and the annual budgetary cycle. To improve strategic planning, participants made a number of other suggestions, many of which derive from the toxicities and failures of the existing strategic‐planning process. Companies should first ensure that their business definition and associated mission statement are broad. Narrow definitions are likely to reduce a company’s identity to its current business model, thereby impeding the possibility of renewal. Companies should also explicitly include innovation in the strategic‐planning process. A chief innovation officer – a new senior‐level appointee in the company – can help ensure that innovation remains central to the strategic‐planning process. Greater scrutiny of strategic plans can also help. For example, CEOs can reject strategic plans that do not include a substantial amount of innovation. The introduction of new metrics for innovation would help formalize this commitment to innovation. Participants also recommended that companies find ways to dissociate the strategic‐planning process from an annual schedule. Instead, the process needs to become continuous. To this end, some participants advocated renaming the process strategic evolution instead of strategic planning.

The purpose of this paper is to discuss process philosophy’s potential contributions to understanding and investigation of innovation processes associated with organizational contexts.

The paper is a theoretical piece that examines the concept of process philosophy by relating it to the relevant literature and use of examples.

In particular, the authors develop some ideas and encourage future discussion around two aspects: process philosophy-oriented conceptualizations of innovation processes and process philosophy-oriented methods of investigation about innovation processes. The authors conclude that more process philosophy-oriented research of innovation processes must be conceptually multidimensional and methodologically performative.

There is a recent claim about a “process turn” within organization studies, which is partly represented by attempts to develop and apply a deeper meaning of process. The presentation of the concept is novel, and does add to the literature. These aspects provide clarification regarding implications of thinking and enquiring procedurally into innovation processes.

This research aims to elucidate the interplay between implementing digital strategies, adopting big data analytics–artificial intelligence (BDAAI), and business process innovation, with a particular emphasis on assessing the moderating impact of digital culture. This article explores how big data analytics can provide organizations with the tools and resources to utilize their data assets effectively, fostering novel and innovative processes.

The research employed a questionnaire-based approach to collect data from managers in Pakistan’s telecom sector. Data analysis was conducted using SPSS and AMOS software. The measurement model’s suitability was assessed via confirmatory factor analysis (CFA) using AMOS.

Preliminary results indicate a correlation between these critical factors: digital strategy prioritization, big data analytics incorporation and digital culture cultivation. The study results confirm the effect of digital strategy on business process innovation and support the positive mediating role of the adoption of BDAAI and the moderating role of digital culture.

The research offers insights but is limited to Pakistan’s telecom industry. Digital strategy is crucial for innovation amid digital transformation, with BDAAI enhancing process innovation. The digital culture in telecom will drive industry digitalization through BDAAI in business innovation.

Leveraging artificial intelligence within a digital culture context could be a productive tool for improving business processes. This research represents a novel exploration of the intersection between digital strategy, BDAAI and digital culture within the context of the telecommunications industry.

Digitalization in precision agriculture incorporates state-of-the-art digital technologies. The transformation requires manufacturers to launch digital platforms and services. As a result, innovation ecosystems emerge. In turn, digital technologies introduce novelty into innovation processes. This socio-technical transition is critical to understanding Digital Service Innovation (DSI). Thus, it is necessary for a micro-founded analysis that biographizes the socio-technical assemblages between routines, artifacts and humans that emerge from DSI processes. Against this backdrop, this study aims to examine the co-production processes and the configuration of digital servitization ecosystems based on Routine Dynamics and Sociomateriality views and the DSI perspective.

The study builds on multiple in-depth case studies, including three precision agriculture machinery and services ecosystems. For each ecosystem, the biography of the co-production process of the technological solution that integrates different actors and artifacts as a sociomaterial assemblage is reconstructed. The qualitative data consist of in-depth interviews with managers in the case companies and stakeholders in each ecosystem. Three ecosystems were surveyed. Documentary information from websites and technical documents on the products and services were systematized and incorporated into the analysis as a form of triangulation.

The analysis of ecosystem biographies evidences that DSI processes involve co-production between routines, actors and artifacts. This co-production implies moving from technology transfer relationships to the co-production of technological solutions oriented to digital servitization. New actors, digital artifacts and changes in user practices emerge as translators of DSI processes toward digital servitization. Thus, the emergence of technological solutions must be understood as socio-technical assemblages. The firms develop digital artifacts that allow the DSI process. The role of digital service platforms and users is critical in co-production. Digital artifacts based on algorithmic technologies perform automation and augmentation routines.

The study provides a complementary viewpoint between DSI approaches and the literature on Routine Dynamics and Sociomateriality. It explains in a micro-founded perspective, and based on biographies of the emergence of DSI ecosystems, how the relationship between digital artifacts, human practices and routines become dynamic in the co-production of Technological Solutions. This perspective proposes that DSI processes are based on the co-production of socio-technical assemblages. Thus, sociomateriality is at the center of analyzing the role of artifacts and the networks of relationships they perform and configure with humans, generating strategies, organizational practices and heterogeneous routine dynamics.

The main aim of this study was to determine the scope of application for an innovation management assessment framework (IMAF) within innovation management. Two distinct geographical regions: ANZ (Australia and New Zealand) and TMSV (Thailand, Malaysia, Sri Lanka and Vietnam) were chosen to identify and analyse significant comparative differences.

A quantitative research approach was used using empirical data collected through an online questionnaire-based survey. Statistical data analysis, including descriptive statistics, univariate analysis of variance (ANOVA) and Hsu’s MCB (multiple comparisons with/to the best) post-hoc test results, was carried out to identify significant differences and similarities in innovation management capabilities within the two regions.

The descriptive statistics analysis reveals areas for potential enhancement in all the model constructs within both regions under investigation. Significant differences in innovation management capabilities between the two regions are identified, specifically in the design for lean six sigma (DFLSS) and the operation function commercial (COMM). However, when considering the remaining constructs of the IMAF, no significant differences in innovation management capabilities are observed between the regions.

The research is limited by the relatively small size of survey questionnaire responses from the targeted population. Future studies could be conducted on a global scale to increase responses.

The research findings serve as valuable insights into the development of important and useful guidelines for managers and innovation practitioners engaged in innovation planning and management.

The critical analysis conducted in this research, focusing on innovation management capabilities by region, represents a significant advancement in the existing body of work that utilises the IMAF model.

This purpose of this study is to explore how the public client, the engineering consultant and the sub-consultant perceive and address innovation outcomes and processes in the planning and design phase of infrastructure projects.

An explorative single-case study of a public infrastructure project was carried out. A multi-dimensional innovation framework, including innovation outcomes and processes, was applied.

Innovation outcomes in terms of products and processes from the engineering consultant’s work in the planning and design phase hold untapped potential for enhancing innovation in construction projects. Additionally, findings indicate that sub-consultants from outside the construction industry (i.e. external knowledge resources) play a crucial role in introducing more radical innovations based on adaptation.

The findings provide an empirical illustration of a further developed innovation framework that can be used by both scholars and practitioners to analyze and discuss the multi-dimensional concept of innovation in the construction context. Additionally, the findings highlight the importance of capturing perceptions of various actors regarding innovation outcomes and processes to facilitate innovation in an inter-organizational context.

The number of small and medium-sized manufacturing companies that have successfully embraced the digital transformation envisioned by the Fourth Industrial Revolution (Industry 4.0) remains low. This paper argues that one reason is the significant innovation required in manufacturing systems to undergo such a transformation. This innovation demands capabilities vastly different from those traditionally employed for continuous improvements in manufacturing systems. The conventional development of manufacturing systems emphasizes resilience, robustness, and efficiency, typically thriving in stable and predictable conditions. However, developing a manufacturing system under highly complex and unpredictable circumstances requires new capabilities. We term this “manufacturing innovation”. At this stage, learning from successful cases is a valuable step towards unifying scattered evidence and developing coherent knowledge of how SMEs successfully do manufacturing innovation in the context of Industry 4.0.

We conducted a multiple case study involving seven small and medium-sized Danish manufacturing companies to investigate successful manufacturing innovation in the context of Industry 4.0. Cross-case analysis identified four critical propositions regarding the capabilities contributing positively to manufacturing innovation.

The research findings highlight various capabilities for successful manufacturing innovation in the context of Industry 4.0. They suggest that such significant digital transformation of manufacturing systems begins with radical innovations in enabling processes rather than core processes. A flexible approach facilitates it, often operationalized through iterative methods. Moreover, the accumulation of knowledge from previous manufacturing innovation initiatives forms a foundational basis for strategically approaching Industry 4.0, suggesting that experience in manufacturing development generally enhances the capacity to adopt Industry 4.0 technologies effectively.

The results underscore the need for viewing digital transformation towards Industry 4.0 as a manufacturing innovation process, which relies on significantly different organizational capabilities than those supporting continuous manufacturing development. This insight has two implications for research in this domain; (1) Innovation process models must be developed to support radical systemic innovation, gradual learning and agile processes in manufacturing, and (2) Industry 4. 0 technologies enable new potential, but the actualization of this potential is dependent on organizational competences.

The findings also offer several practical implications. Identifying patterns of best practices provides much-needed inspiration and insight into how manufacturing innovation for Industry 4.0 may be approached. While we agree with studies showing that competencies are one of the biggest challenges for companies to get started, our results also suggest that by using a flexible approach, companies can build competencies gradually and as needed, which can yield the right results over time. Furthermore, the findings suggest that a specific starting point for manufacturing companies may be enabling processes rather than core processes. This new understanding of the types of solutions companies manage to progress with may suggest that the technologies here are more mature or that there is greater motivation to get started. This implication is supported by the result that a long-term strategy is needed, but that it must be operationalized into smaller solutions to avoid biting off more than they can chew initially. While other researchers have also pointed this out, we provide a deeper understanding of why it is necessary and how it can be operationalized.

The article is one of the first to make a qualitative study on multiple cases to understand how manufacturing companies successfully introduced manufacturing innovation for Industry 4.0.

This paper aims to investigate how digital orientation affects digital process innovation from the dual perspectives of knowledge and capability. It also stresses the mediating effects of digital knowledge creation and strategic flexibility on this relationship, as well as the moderating effect of strategic flexibility on the relationship between digital knowledge creation and digital process innovation.

This paper adopted knowledge- and capability-based views to develop the theoretical model. A total of 193 samples from China were collected to test the model and hypotheses by the partial least squares structural equation modeling method.

The results indicate that digital orientation promotes knowledge creation and strategic flexibility respectively, which in turn facilitates digital process innovation. Also, the effect of digital knowledge creation on digital process innovation is moderated by strategic flexibility.

This study adopts the dual perspectives of knowledge and capability to deepen the relationship between digital orientation and digital process innovation by introducing digital knowledge creation and strategic flexibility as the crucial links, which responds to the call for attaching importance to digital process innovation.

In this research, we seek to understand the effects of artificial intelligence (AI) and knowledge management (KM) processes in enhancing proactive green innovation (PGI) within oil and gas organizations. It also aims to investigate the moderator role of trust and sustainability in these relationships.

This paper employs a quantitative analysis. Surveys have been gathered from the middle-line managers of twenty-four oil and gas government organizations to evaluate the perceptions of the managers towards AI, KM processes, trust, sustainability measures and proactive measures toward green innovation. Analytical and statistical tools that were employed in this study, including structural equation modeling with SmartPLSv3.9, have been used to analyze the data and to examine the measurement and structural models of this study.

The study results reveal a significant and positive impact of AI utilization, KM processes and PGI within oil and gas organizations. Furthermore, trust and sustainability turn out to be viable moderators affecting, and influencing the strength and direction of AI, KM and PGI relationships. In particular, higher levels of trust and more substantial sustainability commitments enhance the positive impact of AI and KM on green innovation outcomes.

Understanding the impact of AI, KM, trust and sustainability offers valuable insights for organizational leaders and policymakers seeking to promote proactive green innovation within the oil and gas industry. Thus, organizations can increase the efficiency of sustainable product development, process improvement and environmental management by using robust AI technologies and effective KM systems. Furthermore, fostering trust among stakeholders and embedding sustainability principles into organizational culture can amplify the effectiveness of AI and KM initiatives in driving green innovation outcomes.

This study extends the current knowledge by assessing the effect of AI and KM on proactive green innovation while accounting for trust and sustainability as moderators. Utilizing quantitative methods offers a nuanced understanding of the complex interactions between these variables, thereby advancing theoretical knowledge in the fields of innovation management, sustainability and organizational behavior. Additionally, the identification of specific mechanisms and contextual factors enriches practical insights for organizational practitioners striving for a practical understanding of the dynamics of the complexities of sustainable innovation in an AI-driven era.