Big data analytics (BDA) has attracted significant attention from organisations who are seeking to leverage its analytical capabilities to enhance performance. Yet, most BDA investments fail to realise their expected value, indicating that the path for BDA value creation remains unclear; scholars call for a better understanding of the role of BDA use. Therefore, we seek to answer the research question: How does the use of BDA impact business value? We conducted a developmental literature review, resulting in the conceptualisation of the BDA-Use-Value (BDA-UV) framework. The framework consists of 10 constructs: BDA system capabilities, BDA use, BDA users and tasks, BDA organisational capabilities, complementary organisational resources, BDA learning, goaloriented activities, intermediate outcomes, ultimate outcomes, and the internal and external environment. The framework also includes 10 propositions, which illuminate the relationships within the BDA value chain, BDA learning loop, and BDA context. This research contributes to theory by providing a consolidated view of disparate yet commensurate research streams (business value of IT and system use) and is critical in the BDA context where organisations are struggling to leverage BDA's informating potential to derive value.

Generative artificial intelligence (AI) is increasingly utilized in business model innovation (BMI). Large language models support ideation and problem-solving but face challenges such as hallucination and complex reasoning. This study develops ChatBMI, an IT artifact that integrates generative AI into business model development tools (BMDTs) using structured prompts to enhance BMI processes. Following the design science research methodology, we assess existing BMDTs, identify AI leverage points, and implement a structured prompt system. ChatBMI improves business model development by enhancing creativity and reducing reliance on prompt engineering expertise. Our research contributes to innovation literature by demonstrating that structured prompts improve AI performance. We also present a structured approach for BMI prompt generation to guide business model designers in generative AI integration. This study advances AI-driven business model innovation, providing practical insights for academia and industry. Future research should explore AI's evolving capabilities and broader implications for digital transformation and strategic innovation.

Digital platforms have demonstrated their disruptive power across various domains, and an increasing number of manufacturing firms have embarked on establishing digital industrial platforms for emerging technologies like the Industrial Internet of Things. Given the technical and organizational complexity of digital industrial platforms, the ways in which complementors can create value by interacting and engaging with them may differ for these platforms. Drawing on a case study conducted at a large multinational platform provider, the article explores how complementors can use digital industrial platforms to their advantage. To this end, the study adopts the concept of leverage, which refers to the ability of platform ecosystems to exert a significant degree of impact that is disproportionate to the input required. The findings indicate that complementors currently primarily benefit from production leverage, utilizing the platform as a technological foundation for individual solutions. However, the utilization of the platform as a marketplace for innovative and generically distributable applications that benefit from innovation and transaction leverage is limited. This is due to a variety of inhibitors, which the platform owner tries to mitigate via enablers through deliberate governance in the form of boundary resources. By unveiling domain-specific manifestations, inhibitors, and enablers across different types of leverage, the study contextualizes leverage within the domain of digital industrial platforms and stresses the need for adopting platform and ecosystem thinking and providing relational governance mechanisms. In addition, it extends the notion of architectural leverage by emphasizing the leveraging ecosystem and its governance through the deliberate orchestration of complementor engagement and underscoring ecosystem leverage based on value co-creation and coordination.

The Metaverse has emerged as a prominent topic of discussion within the technology industry, presenting a wide array of opportunities for value creation across various businesses. Nevertheless, its value potential remains uncertain due to its early development stage. Our research analyzes 29 cases to address this gap and identify key value drivers for business models of complementors in Metaverse ecosystems. We identified five primary Metaverse-enabled value drivers for complementors (immersive customer engagement, massively scaled user innovation, virtual business efficiency, digital exclusivity, and extended ecosystem collaboration.). We illustrate how these drivers enhance those of ebusiness models, exploring their broader implications for business model innovation and value creation. Our findings provide valuable insights into value-creation opportunities that companies can leverage in the Metaverse, contributing to a better understanding of fundamental value drivers and enabling businesses to navigate this emerging landscape more effectively.

With the growing prominence of digital technologies, the business model concept has become increasingly popular in the digital innovation domain. Research on how digital technologies enable business model innovation has so far mainly taken an inside-out perspective focussing on, for example, an organization's innovation process or dynamic capabilities. Conversely, we present a framework that takes an outside-in perspective focusing on how digital technologies as environmental changes enable business model innovation. This framework emphasizes external enablers, which represent aggregate-level phenomena from which multiple emerging ventures within the context of start-ups or established organizations can benefit. We highlight the path and functions of enablement by explaining how digital technologies as external enablers influence business model innovation through their types, characteristics, mechanisms and roles. Our integrative framework consolidates different but related research topics for digital business model innovation, thereby shaping a research agenda with key questions to advance the field. We also see this framework as contributing to a cumulative tradition, notably by bridging the gap between more generic digital business model research and research into new business models driven by specific digital technologies or innovations.

This study explores enhancing human-AI collaboration by integrating vocal and emotional cues into Artificial Intelligence (AI) systems. As AI becomes integral in professional settings, effective human-AI communication is vital for organizational success. Current AI systems lack the depth of human interaction, primarily using numerical data and text prompts. This research aims to develop AI capable of simulating human communication aspects through a design science approach, creating a text-to-speech system that produces emotionally congruent speech. The goal is to enhance human-AI interactions, making them more akin to human-to-human communication, addressing the potential negative impacts of prolonged human-AI collaboration while considering ethical implications.

This article explains how a major Australian university is partnering with the local IT industry to better prepare IT students for the start of their careers in software engineering through real-world industry projects. We present experiences from three different viewpoints.

The number of organisations that operate multiple business models continues to increase. However, operating multiple business models can be complex, as they often need to be harmonised within a broader portfolio due to their interdependencies. This complexity is exac-erbated by the increasing role of digital technology and data - enabling new opportunities but also coming with related paradoxes. This paper examines the growing body of literature on business model portfolios revealing that they are evolving into a strategic tool for value creation and business performance. While there are concomitant value opportunities arising from com-plementarities and synergies, there are also paradoxes emerging from tensions that need to be considered. Employing a developmental literature review, we present a synthesis of recent empirical case studies to gain insight into the management of business model portfolios. Firstly, we identify different strategic intents: diversifying, sensing, and complementing. Secondly, we distil different themes for value opportunities and paradoxes and categorise them according to a business model framework. Thirdly, we identify and discuss the role of digital technologies and data for business model portfolios. Overall, we contribute to an emerging stream of studies on multiple business models in relation to strategy, innovation and technology. By adopting a ho-listic perspective on the management of business model portfolios, we explore strategic intent, value opportunities and paradoxes, and discuss how portfolios can play a role in strategic man-agement and planning.

Metaverse is expected to be a trillion-dollar market within this decade and to change the nature of entrepreneurship. However, research on metaverse, its opportunities, and challenges, as well as the nature of entrepreneurship remains scarce. This study lays out a framework to explore metaverse-enabled entrepreneurship with its enablers for supply and demand, and technological and social enablers. We show how metaverse enables transformational pathways, i.e., purely virtual, physical to virtual, virtual to physical, and hybrid, and shapes offerings, ventures, and processes. We discuss the implications for entrepreneurship research and lay out a future research agenda so that research can lead and inform practice.

Public agencies have a strong interest in artificial intelligence (AI) systems. However, many public agencies lack tools and frameworks to articulate a viable business model and evaluate public value as they consider investing in AI systems. The business model canvas used extensively in the private sector offers us a foundation for designing a public AI canvas (PAIC). Employing a design science approach, this study reports on the design and evaluation of PAIC. The PAIC comprises three distinctive layers: (1) the public value-oriented AI-enablement layer; (2) the public value logic layer; and (3) the public value-oriented social guidance layer. PAIC offers guidance on innovating the business models of public agencies to create and capture AI-enabled value. For practitioners, PAIC presents a validated tool to guide AI deployment in public agencies.

The compatibility between the business model and AI-enabled value creation is paramount for the sustainability of organizations. The public sector lags the private sector in the race to AI readiness and adoption. Although the concept of the business model for the public sector has previously been discussed, we found a lack of evidence for the process of adaption of the business model as a value creation and capture tool from commercial motives to public value motives. This paper adapts the conventional business model canvas for the public sector as it pertains to the design and development of AI systems. Employing a design-science research approach, we postulate five design principles that public agencies must follow to design and deploy AI-enabled public services.

Given the considerable success of companies such as Apple, Amazon or Airbnb, the term platform is on everyone’s lips today. Accordingly, platforms have long since also found their way into service science. However, mastering the transition from established product-sales-based offerings to platform-based services and business models comes with a multitude of challenges. In a B2B context, incumbent companies need to carefully evaluate how they can benefit from the establishment of platforms, especially in light of the effects on their existing business models and ties to other actors. Hence, we invited scholars with different backgrounds to provide viewpoints on the opportunities and challenges of the transition to platform-based services and business models in a B2B environment. The individual commentaries provide various insights on how to conduct this transition and benefit from it successfully. To do so, they contrast different approaches for establishing and governing ecosystems around platforms, discuss B2B-specific pitfalls and opportunities of platform business models, uncover the supporting role of platforms for smart service development, and stress the importance of platform and ecosystem thinking as a necessary mindset.

Generativity is a technology's capability of producing new outputs without input from the originator. Platforms are important technologies that embrace generativity. While the literature generally assumes that generativity arises from platform governance and high-level platform design, we propose that generativity also arises from a platform's three architectural components: the base, the interface, and the add-ons. Drawing on a case study of the Oracle Cloud Platform, we reveal how generativity emerges through the paradox of stability and flexibility in a platform's architectural components. Further, standardization navigates this paradox by coordinating the dependencies between stability and flexibility across heterogeneous stakeholders.