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Local governments face increasingly complex challenges related to their internal operations as well as the provision of public services. However, research on how they embrace evidence-based approaches such as data analytics practices, which could help them face some of those challenges, is still scarce. This study aims to contribute to existing knowledge by examining the data analytics practices in Kansas City, Missouri (KCMO), a city that has become prominent for engaging in data analytics use through the Bloomberg’s What Works Cities (WWC) initiative with the purpose of improving efficiency and enhancing response to local constituents.

This research conducted semistructured interviews with public servants who had data analytics experience at KCMO. Analysis looked for common and emerging patterns across transcripts. A conceptual framework based on related studies is built and used as the theoretical basis to assess the evidence observed in the case.

Findings suggest that data analytics practices are sponsored by organizational leadership, but fostered by data stewards who engage other stakeholders and incorporate data resources in their analytical initiatives as they tackle important questions. Those stewards collaborate to nurture inclusive networks that leverage knowledge from previous experiences to orient current analytical endeavors.

This study explores the experience of a single city, so it does not account for successes and failures of similar local governments that were also part of Bloomberg's WWC. Furthermore, the fact that selected interviewees were involved in data analytics at least to some extent increases the likelihood that their experience with data analytics is relatively more positive than the experience of other local government employees.

Results suggest that data analytics benefits from leadership support and steering initiatives such as WWC, but also from leveraging stakeholder knowledge through collaborative networks to have access to data and organizational resources. The interplay of data analytics sponsored activities and organizational knowledge could be used as means of assessing local governments’ existing data analytics capability.

This study suggests that data analytics practices in local governments that are implementing a smart city agenda are knowledge-driven and developed incrementally through inclusive networks that leverage stakeholder knowledge and data resources. The incrementality identified suggests that data analytics initiatives should not be considered a “blank slate” practice, but an endeavor driven and sustained by data stewards who leverage stakeholder knowledge and data resources through collaborative networks.

This paper aims to explore the open government data initiative in the Food Protection program area within the New York State’s Department of Health to assess the impacts of opening data in terms of data quality and public value. An ecosystem lens is used to explore the dynamics of actors and their interactions, the processes involved in the program and the consequences such interplay brought forth to data quality.

The data were collected through 15 semistructured interviews with multiple stakeholders from different sectors, such as county officials, administrators and technicians, food sanitarians, data journalists and restaurant owners. At the analysis stage, the ecosystem perspective helped to capture the big picture of the open data actor interrelationships within this community regarding the food service inspections datasets.

Prior research suggests that open data initiatives enhance data quality. However, this study shows how opening data can adversely affect the quality of data. Results are explained by competing dynamics and conflicting interests among open data actors, undermining the expected public value from open data initiatives.

The findings are in contrast with the mainstream open data literature and helps open data scholars to anticipate some currently unexpected results of open data initiatives. Limitations include potential biases associated to interpretation of interview data and that the results are based on a single case study.

This study makes governments and policymakers alert about the possibility of similar open data byproducts and unwanted outcomes and helps them to design more effective open data policies, hence gaining higher economic advantage while lowering costs of open data initiatives.

Detailed open data and open data case studies through the ecosystem perspective are still scarce and can enrich discussions about open data policy design and refinement in the public sector. The data used for this research are not used in any prior papers, and to the best of the authors’ knowledge, this is the first study to identify such adverse effects of data quality that have been reported.

In this chapter, we apply diffusion of innovation theory and the theory of management fashion to examine the diffusion trajectory of human resource (HR) analytics in a U.S. context. We focus on the role mass media plays in influencing the diffusion process and address two research questions. First, does the mass media on HR analytics make observable the positive outcomes of HR analytics and is this related to increasing HR analytics adoption over time? Second, does the mass media on HR analytics show evidence of management trendsetting rhetoric?

We analyze published popular trade, business press, and peer-reviewed academic articles over a decade using a big data discourse analytical technique, natural language processing.

We find preliminary evidence that suggests that although the media has broadcasted positive outcomes of HR analytics, adoption has tailed off. In concert with the tailing off of HR analytic adoptions, the media appears to be recasting HR analytics as solving newer problems such as managing talent. Whether this shift makes a difference has yet to be determined.

Business press appears to influence the adoption process, both by broadcasting positive outcomes and through creating management fashion trendsetting rhetoric.

To promote the use of HR analytics, academic institutions and the HR profession need to train HR professionals in the use and benefits of HR analytics.

We lay the groundwork to improve our understanding of the role media plays in influencing how new HRM practices spread across organizations. We introduce the application of an emerging big data analytic technique, natural language processing, to analyze published media on HR analytics.