Social media data used in the measurement of public services effectiveness: Empirical evidence from Twitter in higher education institutions

Agostino, Deborah; Arnaboldi, Michela

doi:10.1177/0952076716682369

Cited by 27 publications

(25 citation statements)

References 61 publications

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“…In particular, scholars have examined how it can improve efficiency and effectiveness in the delivery of public services (Gamage, 2016). Big Data increases public sector efficiency by delivering savings and boosting productivity (Agostino and Arnaboldi, 2017; Johnes and Ruggiero, 2017). It allows organisations to consolidate information at a lower cost and power up public services that rely heavily on accurate data, such as tax processing.…”

Section: Big Data In Public Administration Researchmentioning

confidence: 99%

Big Data and AI – A transformational shift for government: So, what next for research?

Pencheva¹,

Estève

Mikhaylov

2018

Public Policy and Administration

151

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Big Data and Artificial Intelligence will have a profound transformational impact on governments around the world. Thus, it is important for scholars to provide a useful analysis on the topic to public managers and policymakers. This study offers an in-depth review of the Policy and Administration literature on the role of Big Data and advanced analytics in the public sector. It provides an overview of the key themes in the research field, namely the application and benefits of Big Data throughout the policy process, and challenges to its adoption and the resulting implications for the public sector. It is argued that research on the subject is still nascent and more should be done to ensure that the theory adds real value to practitioners. A critical assessment of the strengths and limitations of the existing literature is developed, and a future research agenda to address these gaps and enrich our understanding on the topic is proposed.2

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Section: Big Data In Public Administration Researchmentioning

confidence: 99%

Big Data and AI – A transformational shift for government: So, what next for research?

Pencheva¹,

Estève

Mikhaylov

2018

Public Policy and Administration

151

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“…Despite this growing literature on SMIs, management scholars have, to date, given little attention to how influencers can be detected or appraised once identified. A proxy for quantifying influencers is sometimes obtained by counting the number of posts they publish, their followers, the hits they receive on social media channels or their connections (Himelboim et al., 2014; Agostino and Arnaboldi, 2017; Djafarova and Rushworth, 2017). The main disadvantage of these approaches is that the different kinds of influence exerted by SMIs, whether determined by the number of connections, posts from followers or something else, collapse into a single pigeonhole labelled influencer.…”

Section: Background Literaturementioning

confidence: 99%

How to quantify social media influencers: An empirical application at the Teatro alla Scala

Agostino

Arnaboldi

Calissano

2019

Heliyon

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A topic of primary importance for organizations is the ability to identify and appraise Social Media Influencers (SMIs), given their key role in affecting conversations and interactions on social media. According to the current research in this area, influencers make up a single category of social media users, but only limited attention has been paid concerning the extent to which they can exert their influence. In this study, the quantification and classification of SMIs is addressed by proposing an advanced methodology based on social network analysis - K-shell decomposition - together with a discussion on the relationship between the different SMI categories and the effect of each type of influencer on the public relation activity of an organization. The developed methodology was tested through an action research project conducted at the Teatro alla Scala of Milan, and the results were then discussed with the management of the opera house. The main finding of this work is that SMIs can be split into writers, authorities or spreaders on the basis of the kind of influence they exert, thereby delivering a precisely focused typology of SMIs. These findings enhance our academic knowledge on analytics applied to social science, while also providing a real case situation where managers make practical use of analytics.

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“…70 Another growth area is in the form of repurposed data from sources such as social media and mobile phone companies. 71,72,73,74 These 'soft' datasets are potentially even more significant than IoT data because they are potentially much cheaper to obtain and process. 75 Fully 54 of our survey respondents (43%) mentioned making use of social media data, often in the context of public relations, whilst 31 (24%) mentioned using third party business datasets such as mobile phone data.…”

Section: New Forms Of Datamentioning

confidence: 99%

Data Science for Local Government

et al. 2019

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Data Science is still in a nascent stage in UK local government work. For example, few authorities are exploiting the potential of machine learning to enhance service delivery, or exploring the use of artificial intelligence to enable different forms of interaction with customers and citizens. Hence there is enormous potential for the use of these techniques to be expanded, and thus to deliver better services to citizens. The key reason for this is that doing 'data science' in local government faces a number of crucial barriers. People we spoke to consistently highlighted the difficulty of finding time (and support from senior management) to produce innovative data science projects. Whilst in theory the context of austerity provides stimulus for innovation, in practice the dramatic reductions in budgets have meant that back-office analysts who have retained their positions are almost exclusively focussed on statutory reporting, with hardly any possibility of engaging in new work (especially with any risk of failure). Despite all these barriers, local government is also a site of considerable innovation, with a huge number of pilot projects in progress in areas such as machine learning, artificial intelligence, data merging and A/B testing. There is often talk of a skills gap in local government, with people unable to hire the staff they need. But we found lots of examples of skilled analysts and business intelligence specialists working on remarkable projects with shoestring budgets. Hence, we would encourage local governments to invest more in the people they currently have by providing them with training and space to innovate, whilst looking less to third party contractors and consultants. (and getting access to more) is tricky, people working in the area should be encouraged to start small and work with what they have, to develop quick proofs of concept, and to not be put off by potentially limited access to data.

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Social media data used in the measurement of public services effectiveness: Empirical evidence from Twitter in higher education institutions

Cited by 27 publications

References 61 publications

Big Data and AI – A transformational shift for government: So, what next for research?

Big Data and AI – A transformational shift for government: So, what next for research?

How to quantify social media influencers: An empirical application at the Teatro alla Scala

Data Science for Local Government

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