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Software tools for business model development hold great promise for supporting business model innovation, but nonetheless, virtually no design-relevant knowledge exists concerning the functions that such tools should possess. As a result, practitioners lack guidance for choosing software tools, and researchers lack a foundation for advancing knowledge on these tools in a cumulative way. To address these issues, we synthesize knowledge from research on software tools for business model development and adjacent fields with the results of an analysis of 24 software tools from practice. Our contribution is threefold. First, we provide a comprehensive taxonomy that identifies 43 characteristic functions of software-based business model development tools. Second, we provide a classification of existing software tools for the taxonomy and, on this basis, third, we derive an agenda for future research. We thus support practitioners’ decision making on tool (re-)design and investment, and provide the foundation for a cumulative stream of research on software tools for business model development.

Business modeling tools are crucial for designing and implementing successful business models. However, there exist instances-which we refer to as the design of ecosystemic business models-in which developing a business model requires simultaneous consideration of both one's own business model and that of ecosystem partners. In these instances, standard business modeling tools focusing on representing business models in isolation may be inadequate. Based on a real-world example of a business ecosystem from the maritime logistics industry, we highlight five significant design challenges for ecosystemic business models. We then reflect on and discuss the extended role of the business model as an instrument for inter-organizational alignment, and draw out three implications for business modeling tools. The objective of this paper is to deduce implications and functional design requirements for business modeling tools from a conceptual perspective.

We investigate the impact of delegating decision making to information technology (IT) on an important human decision bias—the sunk cost effect. To address our research question, we use a unique data set containing actual market transaction data for approximately 7,000 pay-per-bid auctions. In contrast with the laboratory experiments of previous related studies, our research presents the unique advantage of investigating the effects of IT-enabled automated bidding agents on the occurrence of a decision bias in real market transactions. We identify normatively irrational decision scenarios and analyze consumer behavior in these situations. Our findings show that participants with a higher behavioral investment are more likely to violate the assumption of normative economic rationality because of the sunk cost effect. More importantly, we observe that the delegation of auction participation, i.e., actual bidding, to IT significantly reduces the occurrence of the sunk cost effect in subsequent decisions made by the same individual. We can attribute this reduction to the comparably lower behavioral investments incurred by auction participants who delegate their bidding to IT. In particular, by mitigating different contributors of behavioral investments, delegating to IT reduces the likelihood of the occurrence of the sunk cost effect by more than 50%. This paper was accepted by Sandra Slaughter, information systems .

The complexity of development and deployment in today’s IT world is enormous. Despite the existence of so many pre-fabricated components, frameworks, cloud providers, etc., building IT systems still remains a major challenge and most likely overtaxes even a single ambitious developer. This results in spreading such development and deployment tasks over different team members with their own specialization. Nevertheless, not even highly competent IT personnel can easily succeed in developing and deploying a nontrivial application that comprises a multitude of different components running on different platforms (from frontend to backend). Current industry trends such as DevOps strive to keep development and deployment tasks tightly integrated. This, however, only partially addresses the underlying complexity of either of these two tasks. But would it not be desirable to simplify these tasks in the first place, enabling one person – maybe even a non-expert – to deal with all of them? Today’s approaches to the development and deployment of complex IT applications are not up to this challenge. “On-The-Fly Computing” offers an approach to tackle this challenge by providing complex IT services through largely automated configuration and execution. The configuration of such services is based on simple, flexibly combinable services that are provided by different software providers and traded in a market. This constitutes a highly relevant challenge for research in many branches of computer science, information systems, business administration, and economics. In this research note, it is analyzed which pieces of this new “On-The-Fly Computing” ecosystem already exist and where additional, often significant research efforts are necessary.

The authors use the under-recognized income accounting identity to provide an important theoretical basis for using the Cobb-Douglas production function in IT productivity analyses. Within the income accounting identity the authors partition capital into non-IT and IT capital and analytically derive an accounting identity-based Cobb- Douglas form that both nests the three-input Cobb-Douglas and provides additional terms based on wage rates and rates of return to non-IT and IT capital. To empirically confirm the theoretical derivation, we use a specially constructed data set from a subset of the US manufacturing industry that involve elaborate calculations of rates of return -- a data set that is infeasible to obtain for most productivity studies -- to estimate the standard Cobb-Douglas and our AI-based form. Their work also shows how some returns to IT that do not show up in output elasticities can be found in total factor productivity -- the novel ways inputs are combined to produce output.

Today, digital platforms mediating between independent groups of users account for a total market value of about US-$4.3 trillion and an employment base of several million direct and indirect employees (Evans and Gawer 2016). A multi-sided platform (MSP) – in the literature also referred to as two-sided platform, two-sided market, or multi-sided market – constitutes a market that enables interaction between at least two sets of users through an intermediary,where the decisions of each group of users on either side ofthe market affects the outcomes of the users on the other side(s) (Rochet and Tirole 2004; Rysman 2009; Hagiu and Wright 2015). MSPs have impressively demonstrated their disruptive potential in well-established global industries.

PurposeThe purpose of this paper is to identify strategic options and challenges that arise when an industrial firm moves from providing smart service toward providing a platform.Design/methodology/approachThis conceptual study takes on a multidisciplinary research perspective that integrates concepts, theories and insights from service management and marketing, information systems and platform economics.FindingsThe paper outlines three platform types – smart data platform, smart product platform and matching platform – as strategic options for firms that wish to evolve from smart service providers to platform providers.Research limitations/implicationsInvestigating smart service platforms calls for launching interdisciplinary research initiatives. Promising research avenues are outlined to span boundaries that separate different research disciplines today.Practical implicationsManaging a successful transition from providing smart service toward providing a platform requires making significant investments in IT, platform-related capabilities and skills, as well as implement new approaches toward relationship management and brand-building.Originality/valueThe findings described in this paper are valuable to researchers in multiple disciplines seeking to develop and to justify theory related to platforms in industrial scenarios.

Data ecosystems can generate valuable business opportunities, but research on their emergence within specific industries is limited. The cultural event industry is characterized by a multifaceted cultural landscape and a fragmented and heterogeneous market of cultural event platforms. The emerging German cultural data ecosystem, envisioned to share event data in a data space, could foster data-driven innovation and enhance value creation in the cultural event industry. Yet, following the ecosystem-as-structure view, the platforms’ willingness to participate in the cultural data ecosystem depends on whether their business model aligns with at least one of the focal value propositions of the cultural data ecosystem. In this paper, we develop a taxonomy of cultural event platform business models, and derive six archetypes. Additionally, we interview industry representatives of these archetypes to shed light on the benefits and obstacles when participating in the cultural data ecosystem, and to identify potential focal value propositions, corresponding actor roles, and activities. Our work contributes to the discussion on taxonomies of data-sharing business models and the emergence of data ecosystems in the cultural event industry.

The business model concept, although a relatively new topic for research, has garnered growing attention over the past decade. Whilst it has been robustly defined, the concept has so far attracted very little substantive research. In the context of the wide-spread digitization of businesses and society at large, the logic inherent in a business model has become critical for business success and, hence, a focus for academic inquiry. The business model concept is identified as the missing link between business strategy, processes, and Information Technology (IT). The authors argue that the BISE community offers distinct and unique competencies (e.g., translating business strategies into IT systems, managing business and IT processes, etc.) that can be harnessed for significant research contributions to this field. Within this research gap three distinct streams are delineated, namely, business models in IT industries, IT enabled or digital business models, and IT support for developing and managing business models. For these streams, the current state of the art, suggest critical research questions, and suitable research methodologies are outlined.