Journal of Strategic Information Systems最新文献

In this paper, we report on a qualitative exploratory case study of a national-level government-led digital transformation. We depart from most studies on government digital transformation that largely focus on improving existing services, bureaucratic processes, or adopting emerging digital technologies. Instead, we analyze the process of a government-led digital transformation aimed at addressing significant institutional voids within a resource-constrained context. Drawing from 60 interviews with stakeholders in the Ghanaian FinTech ecosystem, we theorize the concept of digital branching strategy as an alternative lens to envisage government-led digital transformation that considers the resource-constrained context and characteristics of governments. Our findings show that governments, especially those in resource-constrained contexts pursue digital transformation through exploring frugal innovations and leveraging established resources, structures, and relationships within an ecosystem. We subsequently develop a process model to explain the mechanisms of a national-level government-led digital transformation. Based on the findings and the model, our study offers critical insights to re-imagine government-led digital transformation in resource-constrained contexts by demonstrating how pursuing a digital branching strategy leads to planned and emergent outcomes because of the generative nature of the transformation.

Machine learning (ML) offers widely-recognized, but complex, opportunities for both public and private sector organizations to generate value from data. A key requirement is that organizations must find ways to develop new knowledge by merging crucial ‘domain knowledge’ of experts in relevant fields with ‘machine knowledge’, i.e., data that can be used to inform predictive models. In this paper, we argue that understanding the process of generating such knowledge is essential to strategically develop ML. In efforts to contribute to such understanding, we examine the generation of new knowledge from domain knowledge through ML via an exploratory study of two cases in the Swedish public sector. The findings reveal the roles of three mechanisms – dubbed consolidation, algorithmic mediation, and naturalization – in tying domain knowledge to machine knowledge. The study contributes a theory of knowledge production related to organizational use of ML, with important implications for its strategic governance, particularly in the public sector.

Unlike traditional employer-employee relationships, contemporary digital platforms use algorithms to control and regulate the crowd workforce. Although prior research has expressed concerns over dehumanization stemming from algorithmic management, limited scholarly attention has been dedicated to exploring how human management can complement algorithmic approaches to address these concerns. Leveraging a case study of an on-demand food delivery platform during the COVID-19 pandemic, we developed a process theoretical model that uncovers several drivers and mechanisms essential for the transition from mechanistic algorithmic management to humanistic algorithmic management. This model elucidates the dynamic substitution and complementarity of human and algorithmic management through four key mechanisms: replacing and dampening, compensating, enabling, and synergizing. It also delineates effective humanistic management actions in scenarios in which algorithmic decisions are insufficient, which contributes to both performance and humanistic outcomes. In the realm of contemporary crowd workforce management, where digital platforms employ algorithms, this research sheds light on the unique and timely insights into the role of human managers in enhancing the strategic and humanistic values of algorithmic technology.

While crowdsourcing idea contests have the potential to harness widely distributed knowledge, the quantity of ideas and the complexity involved in idea assessment create a great effort and challenge for organizations. Drawing on tournament theory and the knowledge recombination perspective, this study proposes a model that can assist organizations in efficiently processing crowdsourced ideas by exploring two aspects: the idea content and the contest competition intensity. Analyzing a rich dataset of 16,057 ideas submitted in 61 socio-economic crowdsourcing idea contests, we find that successful ideas are more likely to stem from more distinctive knowledge while ideas that combine diverse knowledge from a broad set of topics are less likely to be successful in the idea contest. Furthermore, competition intensity weakens the positive relationship between idea distinctiveness and success, while it does not influence idea diversity. This study contributes to the growing crowdsourcing literature and offers practical guidance for crowdsourcing intermediaries and organizations.

The specifics of why and how network relationships influence digital transformations have not yet been fully understood. We address this gap by drawing on the relational view of organisations to conceptualise network relationships as the source of requisite external non-generic complementarities for the development of dynamic capabilities (absorptive capacity, integration effort and big data analytics) essential for effective digital transformation outcomes. We follow a positivist research design to test the proposed hypotheses by collecting survey responses from informants from 183 Australian healthcare organisations. The statistical findings indicate that social capital affects the digital transformation outcomes through full individual mediations of absorptive capacity (0.13*), integration effort (0.13*) and big data analytics capability (0.05*) and a full serial mediation (0.01*). This empirical evidence provides two significant advancements to both theory and practice: a) by linking development of dynamic capabilities required for digital transformations to external non-generic complementarities embedded in network relationships; and b) by delineating specific pathways through which the dynamic capabilities of absorptive capacity, integration effort and big data analytics influence digital transformation outcomes.

The micro-entrepreneurship sector, as the second-largest employment generator in rural areas, plays a crucial role in alleviating poverty. This study explores how social enterprises can assist rural micro-entrepreneurs in mobilizing and leveraging indigenous knowledge to align production with demand. We propose technoficing as a strategic approach to rural micro-entrepreneurship. We conducted a case study of CommunityLink, a social enterprise implementing ICT-enabled practices to facilitate the growth of rural micro-entrepreneurship in India. Our findings reveal knowledge disconnections impeding the development of rural micro-entrepreneurship. Importantly, we propose a four-phase approach of technoficing – basic, internal, external, and offline-online – that can effectively mobilize and leverage indigenous knowledge for rural micro-entrepreneurs with minimal resource investment. We synthesize our findings into a comprehensive process model. Our research contributes to the discourse on ICT for development and the emerging academic dialogue on decoloniality by highlighting the significance of empowering local institutions and practices through the strategic use of off-the-shelf technologies. Practitioners and policymakers can leverage our findings to propel indigenous efforts toward rural development and improve rural livelihoods.

Responding to recent calls, this essay offers a commentary on the framing and definition of organizational digital transformation. We focus on the unique ontology of digital transformation and delineate it from neighboring concepts. Our contention is that, despite its volume, current research remains unclear about how the digital transformation of organizations differs from their IT-enabled transformation. We advocate definitional precision to foster knowledge accumulation and to enable scholars to pursue important research questions that are unique to digital transformation.

Our perspective, grounded in the notion of digital resources, defines digital transformation as the metamorphosis of an IT-enabled organization into a digital organization – one with a specific digital architecture and design principles. A key departure from previous conceptualization is that we characterize digital transformation as a change in digital technology architecture rather than a change from digital technology use. Our paper achieves the following: describes the constructs underpinning this formulation, digital resources and digital organization; justifies their use; and describes what research directions the new perspective promotes. With sound definitions of key constructs, Information Systems scholars have the unprecedented opportunity to lead the way in digital “x” research, making our discipline the reference point for the burgeoning “digital research” literature in related business fields.

This paper discusses two opposing narratives of digitalization. Both these narratives are found in existing literature, and the paper understands them as the utopic and dystopic narratives of digitalization. The former expresses unbridled enthusiasm about the bright side of digitalization, while the latter is predominantly pessimistic, and primarily focuses on the undesirable aspects of digitalization. Observing that scholars tend to subscribe to either one or the other narrative, this paper proposes a more middle ground where these competing narratives can be balanced to usher in a better digital future.

Literature reviews serve diverse purposes, including description, understanding, explanation, and testing. Traditionally – before online databases, full-text search availability, and AI-based search tools – identifying relevant sources might have been considered a valuable contribution. However, top-tier information systems (IS) journals now demand more than descriptive reviews; they require authors to move beyond summarizing existing knowledge toward proposing innovative research directions, important research questions, new concepts, and interesting linkages among concepts. Despite adhering to rigorous methodological guidelines, many authors struggle to make conceptual leaps, that is, to elevate their literature reviews beyond description, to achieve a profound understanding, to provide explanations, or to develop a model. Authors may mistakenly prioritize hard work – like thorough literature search, analysis, and organization – over hard thinking, which is crucial for advancing theoretical contributions. With this in mind, I adopt the view that the literature is indeed qualitative data. I suggest that approaches that help make conceptual leaps in qualitative research can benefit literature review authors searching for inconsistencies in the extant literature and developing new questions, concepts, and linkages. Drawing upon qualitative research (Klag, M., and Langley, A., 2013. Approaching the conceptual leap in qualitative research. International Journal of Management Reviews. 15 (2), 149–166.), I unpack the process of conceptual leaping in the conduct of literature reviews. This process involves navigating dialectic tensions between knowing and not knowing, engagement and detachment, deliberation and serendipity, and self-expression and social connection. Effectively managing these tensions can help authors increase the impact and innovativeness of their literature reviews.