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Management and organizational scholars have paid increasing attention to the interconnections between digital transformation and innovation management in the last decade. However, a highly fragmented understanding of this topic is what we are left with so far. In this editorial, we suggest an approach to open up the black box of the interplay between digital transformation and innovation management by providing a framework that identifies three levels of analyisis (i.e., macro, meso, and micro) along which existing and future research on the topic can be organized. This model encourages scholars to conduct theoretical and empirical studies on how digital transformation affects ecosystems’ structure and governance, how industries and firms compete and organize for innovation in a digitalized world, how the processes for developing new products and services change under the effect of digital technologies, and the implications of digital transformation on managing people and teams involved in the innovation process, among the other topics. We also provide a synthesis of the eight papers published in the Special Issue that this editorial introduces and develop an agenda for future research that will hopefully contribute to encourage and shape future scholarly efforts into this field.

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The innovation process may be divided into three main parts: the front end (FE), the new product development (NPD) process, and the commercialization. Every NPD process has a FE in which products and projects are defined. However, companies tend to begin the stages of FE without a clear definition or analysis of the process to go from Opportunity Identification to Concept Generation; as a result, the FE process is often aborted or forced to be restarted. Koen’s Model for the FE is composed of five phases. In each of the phases, several tools can be used by designers/managers in order to improve, structure, and organize their work. However, these tools tend to be selected and used in a heuristic manner. Additionally, some tools are more effective during certain phases of the FE than others. Using tools in the FE has a cost to the company, in terms of time, space needed, people involved, etc. Hence, an economic evaluation of the cost of tool usage is critical, and there is furthermore a need to characterize them in terms of their influence on the FE. This paper focuses on decision support for managers/designers in their process of assessing the cost of choosing/using tools in the core front end (CFE) activities identified by Koen, namely Opportunity Identification and Opportunity Analysis. This is achieved by first analyzing the influencing factors (firm context, industry context, macro-environment) along with data collection from managers followed by the automatic construction of fuzzy decision support models (FDSM) of the discovered relationships. The decision support focuses upon the estimated investment needed for the use of tools during the CFE. The generation of FDSMs is carried out automatically using a specialized genetic algorithm, applied to learning data obtained from five experienced managers, working for five different companies. The automatically constructed FDSMs accurately reproduced the managers’ estimations using the learning data sets and were very robust when validated with hidden data sets. The developed models can be easily used for quick financial assessments of tools by the person responsible for the early stage of product development within a design team. The type of assessment proposed in this paper would better suit product development teams in companies that are cost-focused and where the trade-offs between what (material), who (staff), and how long (time) to involve in CFE activities can vary a lot and hence largely influence their financial performances later on in the NPD process

Purpose The purpose of this paper is to understand how digital technologies can help healthcare organisations and improve the exploration-exploitation paradox over time. The authors explore inputs, processes and outcomes of implementing digital transformation programs and advance four testable propositions. Design/methodology/approach The authors conducted multiple case studies with embedded units of analysis: digital transformation processes; hospitals; and regional healthcare systems. Primary sources come from 107 semi-structured interviews with key informants within 14 Italian hospitals between 2009 through 2011. Findings Three complementary paths emerge as fundamental to balance exploratory and exploitatory efforts in healthcare: assets digitalisation within hospitals; digitally based process integration; and disruptive decision-making through analytics. Intra- and inter-path characteristics are discussed to show how digital transformation can both move hospital within the exploration-exploitation space. Research limitations/implications By its very nature, this study is exploratory. Notwithstanding the number of cases and interviews, its generalisability is limited. Practical implications Digital transformation programs are fundamental to resolve the tensions raised by the exploration-exploitation paradox. Their implementation leads to better performance (cost reductions, quality improvements). A framework is provided for practitioners to make better decisions. Originality/value This study sheds new light on how digital technologies are actually adopted and adapted in healthcare contexts. It does it by entailing a longitudinal perspective.