In the last decade and a half, social media (SM) tools have had their presence felt in the workplace, across industries and levels of decision-making. Despite research interest, the value of using public SM (PSM) such as Facebook, LinkedIn and Twitter for work-related purposes remains a matter of academic debate, even leading to such tools getting banned in workplaces. We utilize a sample of middle and senior managers engaged in knowledge work in India, to examine how the use of PSM tools in the workplace can enable the crucial capabilities of sensing and seizing that are indispensable in the current fast-paced business environment. Further, we examine the role played by managerial social capital in helping managers develop these capabilities because of social media use. We use a hypothetico-deductive survey-based approach using a sample of 208 managers to examine the relationships between PSM use, managerial social capital, and managerial capabilities of sensing and seizing. The findings of this paper contribute to the emerging literature on business value of social media and help advance the extant scholarly understanding of the value added by PSM use at the managerial level, while also having important implications for practitioners. Possible avenues for future research are also discussed.
Employee compliance is crucial for effective cybersecurity, yet the underlying psychological drivers of risky behaviours remain complex. Deliberate cybersecurity risks can arise through active behaviours (actions) or passive behaviours (inaction). Despite growing conceptual recognition of this distinction, empirical evidence remains limited. This study examines how threat perception and neutralisation differentially shape cybersecurity intentions across these two risk domains. Survey data from 490 UK employees, covering four common cybersecurity behaviours were analysed. The findings show that both perceived threat and neutralisation significantly influence intentions, but in different ways. Neutralisation more strongly predicts active risk-taking, whereas perceived threat is a stronger predictor of passive risk-taking. Moreover, passive risk-taking was reported more frequently than active risk-taking, challenging assumptions that employee-driven cybersecurity vulnerabilities primarily stem from overt policy violations. By identifying distinct psychological mechanisms underlying active and passive risk-taking, this study provides practical insights for the design of targeted cybersecurity interventions. Future studies could examine contextual factors that moderate the interplay between threat perception and neutralisation across risk domains.
Despite the emergence and proliferation of sophisticated digital technologies, such as Artificial Intelligence (AI), work email remains a very popular tool for work communications. And we still do not know how to manage it effectively in our increasingly'boundaryless world'. In this provocation, we challenge dominant prescriptive advice in academic and practitioner literature that largely promotes, and at times enforces, the (re)assertion of (temporal) email boundaries in ways that may contradict the very purpose of contemporary digital communications. We argue that this'boundarylessness' of email activity is largely owed to a communication-centric flow which seems difficult to govern, and we identify three complications (flexibility paradox, time zone trap, invisible metronome) that render existing advice unsuitable (or impractical at best). We then synthesise existing literature and propose three solutions (developing a shared temporal structure, implementing task-time mapping, managing the hybridity-liminality interplay) which are better aligned with the boundarylessness of the contemporary workplace. In closing, we recommend four areas of future research (going beyond work email, generational differences in work email use, the role of AI agents in email communications, and the paradoxical impacts of digital work on the future of work).
This study addresses a significant gap in Information Systems (IS) research by examining Less Frequent Use (LFU) and discontinuation of IS products, particularly in the context of Social Media (SM) platforms. Previous research has emphasized adoption and continued use, leaving later lifecycle stages underexplored. Building upon the Stimulus-Organism-Response (S-O-R) framework, this study proposes a novel LFU model to explore key determinants, including perceived influencer disengagement, loss of interest, negative news exposure, addiction realization, and distrust, which contribute to reduced SM usage and the intention to discontinue. Empirical testing of the LFU model reveals that influencer disengagement reduces user interest, leading to less frequent usage and potential discontinuation. Additionally, negative news exposure fosters distrust, diminishing user engagement and leading to discontinuation intent. The results of post hoc analyses provide a comprehensive view of the model for different subsamples, considering variables such as gender, usage frequency, and the number of social media platforms used. The findings have both theoretical and practical implications, offering insights into SM user retention strategies. We also introduce the Integrated Technology Life Cycle framework, which clarifies overlooked stages such as intermittent discontinuance and less frequent use, and outlines directions for future research across diverse technological contexts.
What does it mean to be creative with generative Artificial Intelligence (GenAI) in producing images in visual art and design? An overview is given of saliant prior work on human creativity, machine creativity, human-machine creativity, technology affordances and ethical issues. The author then reports an autoethnographic study of a seven-month project to produce artworks as part of a group project for an exhibition at a regional gallery, including her experimentation with different ways of using image generative-AI (image-GenAI). Insights from the author’s experiences are combined with relevant prior literature to develop guiding principles for assisting creative endeavours in this context. The set of principles, termed ORCA/E for AI-Art, comprise: (1) Openness to alternative perspectives; (2) Reflection and reflexivity; (3) Common communication framework; (4) Affordance-based design; and (5) Ethical and legal concern. Appropriate mechanisms for the principles are identified. The study responds to calls for research in the field of creative human-AI collaboration, which is a fast-changing and important field. The study contributes by adding to the limited number of first-hand accounts of the use of image-GenAI and by proposing guiding principles that address new ways of working creatively with this technology.
The development of complex artificial intelligence (AI) systems presents a compelling knowledge integration challenge to organisations. As the organisations strive to integrate complex domain knowledge into algorithmic models, they also have to arm domain experts with the technical understanding of how such models work so they can be used responsibly. The inscrutability of AI technology – stemming from challenges related to both the technical explainability of the models as well as their social interpretability – makes knowledge integration particularly challenging by creating and deepening knowledge gaps between the AI model, its human users and domain reality. To increase understanding of how such knowledge gaps can be addressed in AI development, this study reports on three qualitative case studies on AI projects faced where inscrutability needed to be managed. Building on the gap model (Kayande et al., 2009), we identify three sociotechnical mechanisms for addressing knowledge gaps related to AI inscrutability and thus facilitating organisational learning. Our work provides contributions to both theory and practice.
Humanity is linguistically diverse, but science is not. Academic success requires English-language mastery. Every major conference, every major journal-even this one-assumes it. English-language bias in science is so strong that it is taken for granted by most scientists and scientific associations, never talked about nor addressed. This is unfair and creates great costs and missed opportunities. It is also unnecessary. Artificial intelligence (AI) translation tools are becoming very good, very fast, allowing us to foresee a multilingual science. Our provocation to readers is: How should we harness AI translation tools for a more impactful, inclusive science? This is a challenge ideally suited to Information Systems scholars because it involves designing sociotechnical artifacts and practices for a better future. To demonstrate feasibility, this article went through a multilingual review process and is published in five languages, all enabled by AI translation.
Large Language Models (LLMs) are increasingly embedded in organisational workflows, serving as decision-making tools and proxies for human behaviour as silicon samples. While these models offer significant potential, emerging research highlights concerns about biases in LLM-generated outputs, raising questions about their reliability in complex decision-making contexts. To explore how LLMs respond to challenges in Information Systems (IS) scenarios, we examine ChatGPT’s decision-making in three experimental tasks from the IS literature: identifying phishing threats, making product launch decisions, and managing IT projects. Crucially, we test the impact of role assignment, a prompt engineering technique, on guiding ChatGPT towards behavioural or rational decision approaches. Our findings reveal that ChatGPT often behaves like human decision-makers when prompted to assume a human role, demonstrating susceptibility to similar biases. However, when instructed to act like AI, ChatGPT exhibited greater consistency and reduced susceptibility to behavioural factors. These results suggest that subtle prompt variations can significantly influence decision-making outcomes. This study contributes to the growing literature on LLMs by demonstrating their dual potential to mirror human behaviour and improve decision-making reliability in IS contexts, highlighting how LLMs can enhance efficiency and reliability in organisational decision-making.
The start-up ecosystem in Australasia, encompassing Australia and New Zealand, has demonstrated significant potential for innovation and growth. However, start-ups in this region face substantial challenges in achieving sustained growth, including limited access to capital, insufficient market knowledge, and the complexities of navigating regulatory environments. This article explores these unique challenges and examines how Information Technology (IT) service providers have emerged as central players in the start-up ecosystem, offering strategic consulting services that encompass growth and expansion. By analysing the case of Tata Consultancy Services' (TCS) Business as a Service (BaaS), the article investigates the dynamics of power and the strategies utilized by both start-ups and service providers to sustain beneficial positions within these partnerships. Utilizing a multi-theoretical lens, the article examines how strategic partnerships between start-ups and IT service providers can foster a thriving entrepreneurial ecosystem in Australasia. The findings emphasize the importance of these partnerships in creating mutual dependencies that enable growth and expansion. However, it also highlights the potential risks associated with such dependencies, urging start-ups to carefully weigh the benefits and challenges of engaging with IT service providers for strategic consulting services. This controversy accentuates the critical debate on whether start-ups should embrace these partnerships or avoid them due to the inherent risks, turning it into a key decision point for their strategic decisions.
Unintended consequences in Information Systems (IS) research are typically examined only after they have surfaced in practice, often treated as isolated instances rather than being systematically identified and understood. This reactive stance constrains our ability to anticipate risks, recognize emerging opportunities, and build cumulative insights across studies. To assess the current state of IS research on unintended consequences, we conducted a scoping review of literature in the Association for Information Systems' Senior Scholars' Basket of Journals. Our review highlights significant gaps in how unintended consequences are defined, theorized, and systematically compared. To address these gaps, we propose three foundational considerations: (1) conceptual clarity, to refine definitions and delineate boundaries; (2) relational configurations, to analyze the interplay between actors, technologies, and contexts; and (3) temporal configurations, to capture the evolving nature of consequences over time. Building on these dimensions, we introduce a set of guiding questions designed to provide shared analytical vocabulary rather than a prescriptive framework. These questions enable researchers to more systematically identify, categorize, and compare unintended consequences across contexts, thereby fostering theoretical precision, facilitating cross-contextual learning, and supporting a more anticipatory and comprehensive understanding of the phenomenon.
Robotic process automation (RPA) is increasingly adopted as a relatively inexpensive automation solution to reduce routinised and repetitive tasks and to initiate an organisation’s broader automation programme. Prior research has focused on highlighting RPA benefits for organisations with suggestions on how to maximise benefits and avoid challenges in RPA implementation. There is less understanding of the emergent and dynamic nature of RPA implementation. Drawing on key elements of socio-technical change, we conducted a process study of RPA implementation in a university. From our analysis, we identified five process patterns: initiation, mobilisation, configuration, adaptation, and evaluation, each of which has different implications for organisational trajectories of RPA implementation. Our findings also offer insights into how the changing role of RPA as an epistemic, technical, and agentic object is intertwined with the dynamics of automation and augmentation in RPA’s conception, development, incorporation into work routines, and evaluation of the initiative’s future.
Process automation has been a cornerstone of organisational success, driving competitiveness through enhanced effectiveness and efficiency. While automation remains a key focus, the rise of artificial intelligence (AI), machine learning (ML) and the Internet of Things (IoT) provides entirely new process design options and as such demands new skills and approaches in Business Process Management (BPM) to maintain relevance and competitiveness. This paper extends current BPM paradigms by introducing the concept of “process autonomisation,” a new paradigm that empowers organisations to make decisions autonomously using real-time data and adaptive business contexts. By critically examining existing BPM methodologies and highlighting key innovations, we propose strategies for evolving process management frameworks - emphasising agility, data-driven decision-making, and seamless digital integration. To thrive in the digital age, we argue, organisations must rethink their BPM practices. We call on both academic and industry stakeholders to collaborate on pioneering advancements in BPM and co-develop a roadmap that will help organisations align their process management with the ongoing digital transformation reshaping industries.
'HyFlex' is a catchword for course designs in higher education that provide students with the opportunity to attend course sessions in-person as well as remotely ('hybrid') and to change the mode of attending every week based on their circumstances and preferences ('flexible'). Due to the need to teach the complex skill of conceptual enterprise modelling (CEM) through practical exercises and feedback, HyFlex course designs for CEM courses pose several challenges to typical CEM-related learning outcomes. This paper proposes a set of design requirements, principles, and features for a comprehensive HyFlex course architecture for CEM courses. The design requirements, principles, and features evolved through and are evaluated against several iterations of CEM courses across two different programmes (undergraduate and postgraduate), student numbers (ranging from 17 to 121), settings (lecture & workshops, block mode, etc.), and course topics (business process and enterprise architecture modelling). The student performances and course evaluations indicate that the offered courses that instantiated the presented course architecture have been both effective and appreciated by the students. Other instructors can draw on our course architecture to design or adapt their own CEM courses – but also, to an extent, courses with other topics – to the HyFlex paradigm.
This paper examines the impact of algorithmic management (AM) tools in the increasingly popular hybrid workplace and the emergence of novel leadership practices in hybrid work settings. Particularly, we explore the use of an Employee Experience Management (EXM) platform – Microsoft Viva – that has AM features in enabling emerging leadership practices influenced by algorithms. Using a qualitative approach with a case study design of a multinational organisation that adopted an EXM platform, the study findings reveal that AM tools contribute to emerging leadership practices that reflect inclusive, humanised, and multimodal modes of leadership – all of which are underpinned by analytics-enabled leadership. The combination of these modes of leadership and associated practices is believed to respond to the challenges in employees’ work environment and enhance the employee experience in a hybrid workplace.
One challenge that organisations face today is how to develop organisational agility to remain competitive within a constantly turbulent, dynamic, and potentially disruptive environment. In this paper, we examine how a software development company operationalises Kotter’s concept of a dual operating system to support organisational agility. The first operating system, the traditional hierarchical structure, enables the company to focus on providing efficiency and stability in its core operations. The second operating system prioritises flexibility. It is operationalised via boundary spanning and knowledge brokering activities rooted in a dedicated organisational unit that enables rapid response to environmental changes. We analyse the unit’s and its members’ approach and identify well-functioning activities and practices contributing to organisational agility as well as challenges and problems. As such we contribute to an improved understanding of how an intertwined strategy of boundary spanning and knowledge brokering, operating alongside a traditional hierarchical structure, forms a balancing mechanism between flexibility and stability, thus enabling organisational agility.
Imagine a world where the boundaries between physical and digital realities dissolve, creating immersive experiences that transform how we interact. This potential lies at the heart of the Metaverse, and a confluence of technologies such as AR, VR, AI and blockchain will be the key to unlocking it. We employ a novel combination of horizon scanning and narrative development to explore the transformative role of AR within the Metaverse. This approach reveals potential technology-driven futures, highlighting emerging trends and disruptions not readily apparent through traditional forecasting methods. Our narratives of the future offer surprising glimpses into potential sociotechnical futures, informing a research agenda for both industry and academia.
Because of globalization and technological advancements, organizations have adopted virtual work arrangements, specifically Global Virtual Teams (GVTs). This study conducted a 16-month ethnographic inquiry in a multinational enterprise to explore team engagement in GVTs. The findings indicate that GVT members often handle multiple roles across various teams and organizations and identify with these entities separately, thus displaying different levels of identification with roles, teams, and organizations. These three identification cascades affect other members' engagement and overall team engagement. Higher levels of identification with roles, teams, and organizations trigger a positive engagement contagion across the GVT, whereas a lower level of identification triggers a negative engagement contagion. We also identify four distinct configurations of GVT members, illustrating the complex nature of engagement dynamics in GVT settings. This identification-based understanding of engagement in GVTs contributes to the literature on team engagement and IS by highlighting the significance of understanding the sensitive dependence of GVT team engagement on members’ identification with their roles, teams, and organizations and subsequent engagement contagion.
Humanity is linguistically diverse, but science is not. Academic success requires English-language mastery. Every major conference, every major journal – even this one – assumes it. English-language bias in science is so strong that it is taken for granted by most scientists and scientific associations, never talked about nor addressed. This is unfair and creates great costs and missed opportunities. It is also unnecessary. Artificial intelligence (AI) translation tools are becoming very good, very fast, allowing us to foresee a multilingual science. Our provocation to readers is: How should we harness AI translation tools for a more impactful, inclusive science? This is a challenge ideally suited to Information Systems scholars because it involves designing sociotechnical artifacts and practices for a better future. To demonstrate feasibility, this article went through a multilingual review process and is published in five languages, all enabled by AI translation.
