Journal of Strategic Information Systems最新文献

Open data platforms freely provide citizens with access to public data, thus enabling improved governance transparency, enhanced public services, and increased civic engagement. However, unlocking the potential of this digital transformation strategy requires that public institutions manage the tension between public and private interests. Furthermore, even when public institutions break down traditional barriers for citizens’ access to data, the potential users often lack the knowledge to leverage it in meaningful ways. Open data platforms therefore tend to fall short of expectations. Leveraging a 10-year action design research study (ADR) in the Swedish Transport Administration (STA), this paper develops design principles for creating value-generating open data platforms in the public domain. The ADR project was initiated to assist STA in its efforts to deal with outlaw innovators who scraped train data from different websites to develop travel apps. Through three iterative design cycles that eventually led to the formation of a new open data platform, the outlaw innovators increasingly became valued partners in the digital transformation process. Theorizing this development process, this paper offers three design principles that provide guidance to public institutions aspiring to digitally transform by making public data accessible. We also reflect upon how these institutions might mitigate the risks associated with partnering with outlaw innovators in the pursuit of an open data strategy.

For many public sector organisations, digital transformation is a strategic priority. However, there is limited understanding of how everyday practices shape such large-scale transformation. To address this, we adopt a strategy-as-practice approach to capture the ‘doings’ of strategy on the ground and the role this plays in large-scale transformation. We conducted an in-depth interpretive case study on UK policing and collected rich data from multiple sources. This is an important context as the police face increasing demands to deliver digital transformation while maintaining a high level of service to protect the public. Our findings reveal that public sector organisations like the police find themselves in a state of liminal digital transformation. We conceptualise this liminality as incomplete, contested, and localised, due to the specific conditions in the strategy practices: openness of strategy, ambiguity in rules and norms, and interdependencies across organisational boundaries. We theorise this relationship in a model of ‘liminal digital transformation’ and propose a set of propositions. By doing so, our research introduces a novel perspective on digital transformation in the public sector and how it is shaped by everyday strategising practices.

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.