We examine how firms configure alliance portfolios—that is, networks of partnering firms—in order to exchange, share, or codevelop the capabilities they require to engage in digital innovation. We analyze data from 550 U.S. firms and the strategic alliances they formed within and across industrial sectors to study how the configuration of alliance portfolios in terms of size, degree of exploration, internationality, and competition affects the volume and quality of digital patents. We find that alliances appear to be an effective means, yet alliances for digital innovations require a different configuration when compared with alliances for non-digital innovations. Large and explorative alliance portfolios help with the creation of digital innovations while international alliances and alliances involving competitors do not. We discuss the implications of these findings for research on digital innovation and alliances. We also distill practical advice to executives charged with making strategic decisions about inter-firm partnerships.

Corona dashboards are interactive geospatial information systems used by billions of users to help them understand the evolution of the COVID-19 pandemic. I use a representational lens to explore how these systems can be made more useful. With this lens, the usefulness of these systems to convey information about the pandemic fundamentally depends on whether these systems are implemented as representation or state-tracking systems. I suggest that corona dashboards presently focus disproportionally on representing socially constructed properties (infection rates, deaths, levels of vaccination) of various things such as people, regions, or countries. They would become more useful if they additionally focused on tracking events (such as policy implementations) and changes in states (such as capacities of lockdown wards, usage of face masks). By applying a methodology for design science research involving design archaeology, I analyse the in situ implementation of Germany’s RKI COVID-19-Dashboard, develop new design principles to extend the state-tracking abilities of corona dashboards, and explore the importance, actability, and effectiveness of these design principles through an empirical case study. The contributions this paper makes are new and validated design principles for new feature implementations that can help making corona dashboards more effective and useful.

The world is blazing with change and digital innovation is fuelling the fire. Process management can help channel the heat into useful work. Unfortunately, research on digital innovation and process management has been conducted by separate communities operating under orthogonal assumptions. We argue that a synthesis of assumptions is required to bring these streams of research together. We offer suggestions for how these assumptions can be updated to facilitate a convergent conversation between the two research streams. We also suggest ways that methodologies from each stream could benefit the other. Together with the three exemplar empirical studies included in the special issue on business process management and digital innovation, we develop a broader foundation for reinventing research on business process management in a world ablaze with digital innovation.

This special issue contains extended versions of the outstanding papers presented at the 13th International Conference on Business Process Management (BPM) that took place in Innsbruck, Austria on August 31–September 3, 2015.

Conceptual models are fundamental to system analysis and design. However, the actual usage of conceptual models remains poorly understood, in particular, how and why practitioners would use multiple models in combination when doing their work. In this paper, the authors explore the reported use of multiple conceptual models for system analysis and design to determine the circumstances that lead professionals to use multiple models. They uncover both semantic and pragmatic reasons that influence the choice and selection of different models for system analysis and design tasks. Contrasting these findings to existing ontological theories, the authors find that the extent and type of multiple model use is determined by not only ontological factors but also contextual factors that can override ontological qualities and in so doing bring forth desired qualities for users. The authors offer several novel propositions about the implications of ontological theory that will be worth exploring in future research.

Enterprise Architecture Management (EAM) is discussed in academia and industry as a vehicle to guide IT implementations, alignment, compliance assessment, or technology management. Still, a lack of knowledge prevails about how EAM can be successfully used, and how positive impact can be realized from EAM. To determine these factors, we identify EAM success factors and measures through literature reviews and exploratory interviews and propose a theoretical model that explains key factors and measures of EAM success. We test our model with data collected from a cross-sectional survey of 133 EAM practitioners. The results confirm the existence of an impact of four distinct EAM success factors, ‘EAM product quality’, ‘EAM infrastructure quality’, ‘EAM service delivery quality’, and ‘EAM organizational anchoring’, and two important EAM success measures, ‘intentions to use EAM’ and ‘Organizational and Project Benefits’ in a confirmatory analysis of the model. We found the construct ‘EAM organizational anchoring’ to be a core focal concept that mediated the effect of success factors such as ‘EAM infrastructure quality’ and ‘EAM service quality’ on the success measures. We also found that ‘EAM satisfaction’ was irrelevant to determining or measuring success. We discuss implications for theory and EAM practice.

Process modeling grammars are used to create scripts of a business domain that a process-aware information system is intended to support. A key grammatical construct of such grammars is known as a Gateway. A Gateway construct is used to describe scenarios in which the workflow of a process diverges or converges according to relevant conditions. Gateway constructs have been subjected to much academic discussion about their meaning, role and usefulness, and have been linked to both process-modeling errors and process-model understandability. This paper examines perceptual discriminability effects of Gateway constructs on an individual's abilities to interpret process models. We compare two ways of expressing two convergence and divergence patterns – Parallel Split and Simple Merge – implemented in a process modeling grammar. On the basis of an experiment with 98 students, we provide empirical evidence that Gateway constructs aid the interpretation of process models due to a perceptual discriminability effect, especially when models are complex. We discuss the emerging implications for research and practice, in terms of revisions to grammar specifications, guideline development and design choices in process modeling.

Unstructured text data, such as emails, blogs, contracts, academic publications, organizational documents, transcribed interviews, and even tweets, are important sources of data in Information Systems research. Various forms of qualitative analysis of the content of these data exist and have revealed important insights. Yet, to date, these analyses have been hampered by limitations of human coding of large data sets, and by bias due to human interpretation. In this paper, we compare and combine two quantitative analysis techniques to demonstrate the capabilities of computational analysis for content analysis of unstructured text. Specifically, we seek to demonstrate how two quantitative analytic methods, viz., Latent Semantic Analysis and data mining, can aid researchers in revealing core content topic areas in large (or small) data sets, and in visualizing how these concepts evolve, migrate, converge or diverge over time. We exemplify the complementary application of these techniques through an examination of a 25-year sample of abstracts from selected journals in Information Systems, Management, and Accounting disciplines. Through this work, we explore the capabilities of two computational techniques, and show how these techniques can be used to gather insights from a large corpus of unstructured text.

Process modeling grammars are used by analysts to describe information systems domains in terms of the business operations an organization is conducting. While prior research has examined the factors that lead to continued usage behavior, little knowledge has been established as to what extent characteristics of the users of process modeling grammars inform usage behavior. In this study, a theoretical model is advanced that incorporates determinants of continued usage behavior as well as key antecedent individual difference factors of the grammar users, such as modeling experience, modeling background and perceived grammar familiarity. Findings from a global survey of 529 grammar users support the hypothesized relationships of the model. The study offers three central contributions. First, it provides a validated theoretical model of post-adoptive modeling grammar usage intentions. Second, it discusses the effects of individual difference factors of grammar users in the context of modeling grammar usage. Third, it provides implications for research and practice.

Business process modeling is widely regarded as one of the most popular forms of conceptual modeling. However, little is known about the capabilities and deficiencies of process modeling grammars and how existing deficiencies impact actual process modeling practice. This paper is a first contribution towards a theory-driven, exploratory empirical investigation of the ontological deficiencies of process modeling with the industry standard Business Process Modeling Notation (BPMN). We perform an analysis of BPMN using a theory of ontological expressiveness. Through a series of semi-structured interviews with BPMN adopters we explore empirically the actual use of this grammar. Nine ontological deficiencies related to the practice of modeling with BPMN are identified, for example, the capture of business rules and the specification of process decompositions. We also uncover five contextual factors that impact on the use of process modeling grammars, such as tool support and modeling conventions. We discuss implications for research and practice, highlighting the need for consideration of representational issues and contextual factors in decisions relating to BPMN adoption in organizations.