Thomas Huber, ESSEC Business School

Maximilian Schreieck, University of Innsbruck

Andreas Hein, University of St Gallen

Ilan Oshri, University of Auckland

Julia Kotlarsky, University of Auckland

Over the last years platform partnerships between independent third-party developers (complementors) and providers of software platforms (platform owners) have become the prevailing model for developing and delivering software products and services. Platforms are strategic in enabling value co-creation through boundary resources—such as software development kits (SDKs) and application programming interfaces (APIs)—which provide complementors with the tools needed to seamlessly integrate their offerings into the core platform infrastructure.

Novel and creative uses of technologies such as Artificial Intelligence (AI), Internet of Things (IoT), low-code/no-code platforms, and distributed ledger technologies (DLT) like blockchain are redefining how organizations interact, collaborate, and compete. AI and IoT enable real-time data analytics and predictive insights, allowing platforms to coordinate relationships and optimize sourcing decisions by anticipating demand, managing risks, and adjusting partnerships. IoT enhances this by providing continuous data streams from connected devices, enabling more responsive, data-driven collaborations in logistics and inventory management. Low-code/no-code platforms reduce entry barriers for participants, enabling organizations to develop solutions independently. Blockchain and smart contracts automate partnership agreements through self-executing code, reducing transaction costs and fostering trust, particularly in complex, cross-border environments.

Together, these technologies have strategic implications on partnerships in platform ecosystems, potentially reshaping established practices in value co-creation and appropriation, and sourcing arrangements. While they offer complementors opportunities to innovate and gain leverage, they also equip platform owners with new tools to reinforce their dominance. Whether these new tools will benefit complementors or increase platform owners' control remains an open question. For example, as decentralized, real-time decision-making becomes more common, will these technologies empower complementors to innovate and operate autonomously, or will they reinforce existing power imbalances, allowing platform owners to strengthen centralized control and dictate terms of value appropriation and collaboration?

As dominant platform owners like Apple, Google, Amazon, and Microsoft in Western markets, and Alibaba, Tencent, and Baidu in China, continue to control key sectors of consumer and business markets, complementors face growing challenges in navigating the advantages of platform participation against the risks of dependency and lock-in, both undermining complementor autonomy. Meanwhile, the evolving regulatory landscape, such as the European Union’s (EU) Digital Markets Act, seeks to address these power imbalances by imposing restrictions on platform gatekeepers that reshape partner strategies and value co-creation.

Indeed, platform partnerships are undergoing significant strategic transformations. Advancements in technologies such as AI, IoT, and blockchain, along with shifts in political and regulatory environments, are influencing how partnerships within platform ecosystems evolve. In this rapidly changing landscape, critical questions arise about the future dynamics of partnerships in platform ecosystems. How will the balance between value co-creation and value appropriation shift as AI, DLT, and IoT become more integrated into platform partnerships? Will these technologies empower complementors to innovate and capture a fair share of the value they create, or will they enable platform owners to centralize control, deepening dependencies and shifting value appropriation in their favor? And as platform ecosystems often nurture sourcing relationships, how and in what ways sourcing relationships between a platform owner and complementors affect the platform ecosystem? Ultimately, how can both platform owners and complementors strategically navigate these changes to ensure mutual benefit in an increasingly complex ecosystem?

This call for papers invites research that explores how these strategic shifts are reshaping platform partnerships, including changes in the strategies and business models of platform owners and complementors, and the governance of these partnerships and ecosystems. We welcome empirical, conceptual, design, and simulation research. Any methodologies and theoretical perspectives are encouraged as long as they contribute to the theoretical understanding of platform partnerships, along with practical insights.

Potential topics

• The role of AI/machine learning and predictive analytics in managing platform partnerships.
• The impact of AI platforms' unique characteristics, such as learning abilities and non-deterministic behaviors, on platform partnerships.
• The role of IoT in real-time coordination and dynamic management of platform partnerships.
• Effects of distributed ledger technologies on platform partnerships.
• Impact of low-code/no-code platforms on the role of complementors within these platforms.
• Strategies for complementors to manage power asymmetries within platform partnerships.
• The role of regulatory interventions in addressing the increasing power of platform owners.
• Strategies for complementors and platform owners to adapt to regulatory changes, such as new laws and antitrust decisions.
• Impact of emerging technologies on managing tensions in platform governance, such as control versus autonomy.
• The role of AI, real-time analytics, and DLTs in shaping sourcing strategies, decisions, and governance.

Timeline

• December 5, 2024: Online information session
• December 15, 2024: Informal meet the SI Editors, SIG digital sourcing, platforms and ecosystems (DSPE) Pre-ICIS workshop
• August 14, 2025: Informal meet the SE editors, SIG DSPE track - AMCIS 2025
• September 15, 2025: Full paper submission
• December 15, 2025: First round decisions
• March 15, 2026: Deadline to submit revised paper
• June 15, 2026: Second round decisions
• September 1, 2026: Deadline to submit revised paper
• October 1, 2026: Final decisions

Julia Kotlarsky, University of Auckland

Ilan Oshri, University of Auckland

Oliver Krancher, IT University of Copenhagen

Rajiv Sabherwal, University of Arkansas

The last decade has seen a significant proliferation of new technologies. These include robotic process automation, big data, machine/deep learning and artificial intelligence, blockchain, and other "emerging technologies". Consulting and market research companies have consistently predicted that the current wave of technologies will transform both front and back office operations, requiring firms to rethink their business models and strategies.

While evidence suggests that firms will continue to invest in emerging technologies, there has so far been little evidence about the involvement of service providers and advisory in the selection, design, and adoption of such emerging technologies. IS sourcing research has conventionally accounted for three main areas of interest: sourcing decision, contractual structures, and relationship management. Emerging technologies challenge these traditional conventions in the IS sourcing literature. Particular challenges include but are not limited to: greater dependency on data in emerging technologies, which triggers clients to source data science services; the actual implementation of emerging technology often happens through platforms that involve multiple players and services; the asset transfer model has become less relevant and yet client firms are still expected to reduce their headcount; there is greater emphasis on the redesign of the value chain as services have become digitized and decisions are now driven by smart algorithms; emerging technologies are viewed as a black box, posing a skills and expertise challenge to client firms to ensure acquiring and retaining knowledge of the technology.

Further, the IS sourcing literature which has traditionally relied on three main reference theories, namely, transaction cost economics, resource dependency, and social exchange. Emerging technologies emphasize the need to expand this theoretical base and accommodate new paradigms of capturing and analyzing data in IS sourcing research. Taking into account the above observations, the IS sourcing literature thus begs for the re-examination of client-supplier-advisory relationships by challenging core concepts of sourcing decision, contractual structure, and relationship management as offering new theoretical landscapes that accommodate the sourcing phenomenon in a digital age.

This special issue seeks to facilitate an empirical and theoretical re-examination of "IS sourcing" in the light of the current wave of emerging technologies. We invite research papers that investigate issues relating to emerging technologies and IS sourcing, particularly focusing on robotic process automation, big data, machine/deep learning and artificial intelligence, and blockchain. Other technologies are less of interest for this special issue.

Potential topics

• Governance and control structures between client-supplier-advisory in emerging technology settings
• Sourcing decision making in emerging technology settings
• Ecosystem and platforms in emerging technology sourcing settings
• Contract management in emerging technology sourcing settings
• Full data lifecycle in emerging technologies and its implications for sourcing management
• Skills and capability development and retention in emerging technology sourcing settings
• Implications for innovations within the client-supplier-advisory eco-system
• Ethical and societal implications deriving from such sourcing settings
• Impact of emerging technologies on client’s, supplier’s and advisory’s strategies, business models and capabilities
• Implications of crowdsourcing, big data, analytics as well as machine/deep learning and artificial intelligence for the contemporary IS outsourcing

Timeline

• April 15, 2021: First round submission deadline
• July 25, 2021: First round decision to authors
• October 25, 2021: Second round submission deadline
• January 28, 2022: Second round decision to authors
• April 15, 2022: Third and final round submissions deadline
• June 15, 2022: Final decision to authors

Nik Hassan, University of Minnesota Duluth

Suzanne Rivard, HEC Montreal

Ulrike Schultze, Southern Methodist University

Leslie Willcocks, London School of Economics and Political Science

Theorizing is one of the defining aspects of academic research. Articulating insights generated from situated empirical observations into relatively stable abstractions (e.g., concepts, propositions, models) that elucidate, explain or predict events occurring at another time and place is the hallmark of knowledge generation (King 2021). Of late, much attention has been paid to theory development in the Information Systems (IS) field. For example, the MIS Quarterly (MISQ) special issue on 'Next-Generation Information Systems Theorizing' reflects a sense of urgency felt within the IS field to make sense of an increasingly unruly and complex reality given the increasing pace of technological innovation (Burton-Jones et al. 2021). In similar vein, two 2021 Journal of Information Technology (JIT) debates sections focused on 'IS Research and Science' (Siponen and Klaavuniemi 2020, and commentaries) and 'Theorising as Craft' (Rivard 2021, and commentaries) and a recent Journal of the Association for Information Systems (JAIS) special issue focused on 'Envisioning Digital Transformation – Advancing Theoretical Diversity'. New and native IS theories ensure that the IS field remains vibrant, diverse, academically and socially relevant and influential (Hassan 2011; Hassan and Willcocks 2021; Lowry et al. 2020; Markus and Saunders 2007). Researchers, however, often set the bar too high for theory delivered within individual research contributions by viewing theory as a comprehensive and detailed explanation of a phenomenon (Rivard 2021), an object that Weick (1995) refers to as a 'theory that sweeps away all others' (p. 386). This mindset may lead IS researchers to adopt theories from other fields as a foundation rather than creatively developing new and native theories from empirical observations (Grover and Lyytinen 2015).

To maintain the long-term growth and vitality of the IS field, a definition of theory that encourages cumulative and novel theorizing over time is needed (Hassan and Willcocks 2021; King 2021). One way of achieving this is by advancing a 'products of theorizing' perspective (Weick 1995). The theorizing perspective celebrates both the final outcomes and the 'interim struggles' that characterize theory generation (Weick 1995). It holds the promise that interim conceptual artifacts of theorizing such as metaphors, frameworks and concepts (Lowry et al. 2020) are valuable to the discipline as they can be leveraged and exploited further in subsequent research. In this way, a discipline might inch its way towards native theories over time. The goal of this special issue is to solicit and showcase exemplars from the 'products of theorizing' approach. We are looking for articles that go beyond verification dimensions of the natural-science approach to IS research (Schlagwein 2021) and push the envelope of theorizing towards native products of theorizing (Hassan et al. forthcoming): theory frame devices (i.e., questions, paradigms, laws and frameworks), theory generators (i.e., myths, analogies, metaphors and models) and theory components (i.e., concepts, constructs, statements and hypotheses).

The time is ripe to embark upon the development of native theories in the IS field, as IT has leaped into the consciousness of the public in unprecedented ways. Big data and AI applications are part of consumers’ and workers’ everyday lives, either enhancing them or increasing their visibility (Aral 2020; Benbya et al. 2021; Zuboff 2019); firms increasingly deploy robotic process automation and cognitive automation, aiming to achieve benefits for themselves, their employees and their customers (Lacity and Willcocks 2021); and governments rely on new forms of data collection about citizens (e.g., blood samples, vaccination data, fingerprints, iris-recognition systems) in support of the services they provide (Kolas et al. 2021). To take advantage of the theorizing opportunities that these emergent information technology (IT) phenomena provide, the goal of this special issue is to encourage research that generates products of theorizing derived from and related to IT phenomena. We are seeking contributions that develop products of theorizing, that is, interim constructions, that are useful as precursors to strong native IS theory. To increase the likelihood of a theorizing product’s value, we are particularly interested in research that tackles phenomena that are core to the IS discipline. Given the multifaceted nature of the IS discipline, we do not expect agreement on what constitutes a core IS phenomenon. We therefore encourage authors to make the case that the products of theorizing they develop are valuable for articulating native theories that are defining of the field of IS or are substantially valuable for their specific area.

Potential topics

• Use of theory generators (e.g., analogies, myths, models and metaphors) for understanding and guiding research of emergent phenomena such as surveillance capitalism, fake news, social media, or the performativity of big data and AI.
• Constructing or formulating novel frameworks derived from or relevant to emerging technologies.
• Adopting a genealogical lens to uncover the products of theorizing that have been instrumental in the theories that have become popular in the IS discipline (e.g., socio-technical, sociomateriality, imbrication).
• Reviewing and analysing the foundations of core concepts of the IS field such as that of 'information,' 'system' and 'technology'.
• Re-theorizing areas or subfields within IS (e.g., design science, data analytics, big data, AI) where native products of theorizing are sparse.
• Problematizing the IS field (i.e., going beyond gap-spotting and borrowing theory from reference disciplines) to identify questions and challenges that the field of IS is uniquely qualified to address.
• Inventing native original concepts in the IS field that will serve as foundations for the scientific IS research method and declare to the world, the field’s core concerns.
• Exploring the role of scientific laws in IS research as a product of theorizing with relevance to emerging phenomena.
• Unpacking products of theorizing (e.g., questions, models and frameworks) that uncover deep-seated assumptions about contemporary IS phenomena (e.g., symbols of value, solidarity, social structure, conflict and contradictions within a technology-driven society) that are especially relevant to emerging technologies.
• Distinguishing between non-observable constructs from more observable concepts that are rarely discussed in IS research but form the mainstay of scientific studies.
• Aggregating the statements, claims and propositions that have been made by a stream of IS literature and that are deemed most impactful on and relevant for contemporary users, organizations and society, to set the stage for the further progress towards native IS theory.

Timeline

• October 24, 2021: Extended abstracts (for workshop and feedback) due
• December 12, 2021: Workshop at ICIS 2021 SIG PHIL
• March 1, 2022: First-round submissions version due
• May 15, 2022: First-round reviews returned
• July 15, 2022: Second-round submissions due
• August 30, 2022: Second-round reviews returned
• October 1, 2022: Final versions due
• October 1, 2022: Final decisions and publication online