Is measuring social innovation a mission impossible?

Mihci, Hakan

doi:10.1080/13511610.2019.1705149

Cited by 21 publications

(18 citation statements)

References 26 publications

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“…Studies in the first group focuses on the conceptual framework of social innovation. Generally, these studies (Bhatt & Altinay, 2013; Carayannis, Grigoroudis, Stamati, & Valvi, 2019; Datta, 2011; Domanski, Howaldt, & Kaletka, 2020; d'Ovidio & Pradel, 2013; Maruyama, Nishikido, & Lida, 2007; Mihci, 2020; Mumford & Moertl, 2003; Oeij, van der Torre, Vaas, & Dhondt, 2019; Pel & Kemp, 2020; Rabadjieva & Butzin, 2020; Slee, 2019; Tracey & Stott, 2017) include the criteria, elements, definitions and theoretical foundations of social innovation.…”

Section: Literature Reviewmentioning

confidence: 99%

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A decision model approach for determining social innovation potential of technological projects

Özdemir¹,

Baki

2021

Multi Criteria Decision Anal

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Since project management is inevitably a social effort involving all relevant actors, social aspects have to be emphasized when considering collaborative project management. Social innovation focuses on reacting to social demands and developing innovative solutions to overcome various social issues. Therefore, determining the social innovation potential in the project management process is very important. The main purpose of this study is to propose a Fuzzy Analytic Network Process (FANP) and Fuzzy VIKOR (FVIKOR)‐based decision model to determine the social innovation potential of technological projects. The results of FANP revealed that the most important criteria for determining social innovation potential of projects are financial resources, technological developments and social capital. Furthermore, FVIKOR was used for ranking social innovation potential of the selected technological projects. There are several managerial implications of this study. First, we suggest that practitioners should integrate the proposed model into their project evaluation processes. Second, they can also use this model to define the social innovation factors during project selection. Lastly, the proposed model can be employed to prioritize the social innovation factors that enable managers to allocate the appropriate resources to project management process.

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Section: Literature Reviewmentioning

confidence: 99%

“…They introduced a theoretical outline that is needed to generate a measurement model for social innovations. Moreover, Mihci (2020) indicated that the problems with social innovation metrics also extended to statistical and methodological spheres.…”

Section: Introductionmentioning

confidence: 99%

A decision model approach for determining social innovation potential of technological projects

Özdemir¹,

Baki

2021

Multi Criteria Decision Anal

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“…Despite the abundant literature about SI, the notion may be blurred too much in the current policy debate [56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73]. As Mihci [74] (p. 18) highlights, insofar as "many theoretical approaches, ideas and practices labelled under the category of SI have relatively little in common", such a vagueness and fuzziness might make the intervention process even more complex and challenging.…”

Section: Social Innovation (Si)mentioning

confidence: 99%

Democratising Smart Cities? Penta-Helix Multistakeholder Social Innovation Framework

Calzada

2020

Smart Cities

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The smart cities policy approach has been intensively implemented in European cities under the Horizon 2020 programme. However, these implementations not only reduce the interdependencies among stakeholders to technocratic Public-Private-Partnership (PPP) models, but also fail to question the identities of strategic stakeholders and how they prioritise their business/social models. These aspects are putting democracy at stake in smart cities. Therefore, this article aims to unfold and operationalise multistakeholders’ policy frameworks from the social innovation perspective by suggesting the ex-novo penta-helix framework—including public, private, academia, civic society, and social entrepreneurs/activists—to extend the triple and quadruple-helix frameworks. Based on fieldwork action research conducted from February 2017 to December 2018—triangulating desk research, 75 interviews, and three validation workshops—this article applies the penta-helix framework to map out five strategic dimensions related to (i) multistakeholder helix framework and (ii) the resulting business/social models comparatively in three follower cities of the H2020-Replicate project: Essen (Germany), Lausanne (Switzerland), and Nilüfer (Turkey). For each case study, the findings reveal: (i) a unique multistakeholder composition, (ii) diverse preferences on business/social models, (iii) a regular presence of the fifth helix as intermediaries, and (iv) the willingness to experiment with democratic arrangements beyond the hegemonic PPP.

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“…This replication-driven policy design took for granted the assumption that the three lighthouse cities could be the only ones spelling out urban solutions, without considering whether, first, the urban implementations in the lighthouse cities were appropriate for the fellow cities, and, second, the fellow cities already had ongoing replicable models worth considering. Instead, this article offers an experimental alternative from the social innovation perspective [22,[41][42][43][44] by offering an enhanced policy approach. That is, interaction and learning among cities and their related stakeholders would be multidirectional, radial, dynamic, iterative, and democratic.…”

Section: Introductionmentioning

confidence: 99%

“…Section 2 examines the policy context until 2019, encompassing 17 lighthouse projects (46 lighthouse cities and 71 fellow cities) with their related replication strategies, city composition, networks, and stakeholders' involvement [50]. Section 3 reviews the literature from the social innovation perspective to shed light on the research question [41][42][43][44]51,52]. Section 4 objectively describes how the City-to-City Learning Programme was established and implemented among the six Replicate cities as an alternative replication pathway by experimenting an enhanced policy approach through fieldwork action research [38,39].…”

Section: Introductionmentioning

confidence: 99%

Replicating Smart Cities: The City-to-City Learning Programme in the Replicate EC-H2020-SCC Project

Calzada

2020

Smart Cities

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This article addresses the problem of replication among smart cities in the European Commission’s Horizon 2020: Smart Cities and Communities (EC-H2020-SCC) framework programme. This article initially sets the general policy context by conducting a benchmarking about the explicit replication strategies followed by each of the 17 ongoing EC-H2020-SCC lighthouse projects. This article aims to shed light on the following research question: Why might replication not be happening among smart cities as a unidirectional, hierarchical, mechanistic, solutionist, and technocratic process? Particularly, in asking so, it focuses on the EC-H2020-SCC Replicate project by examining in depth the fieldwork action research process implemented during 2019 through a knowledge exchange webinar series with participant stakeholders from six European cities—three lighthouse cities (St. Sebastian, Florence, and Bristol) and three follower-fellow cities (Essen, Lausanne, and Nilüfer). This process resulted in a City-to-City Learning Programme that reformulated the issue of replication by experimenting an alternative and an enhanced policy approach. Thus, stemming from the evidence-based policy outcomes of the City-to-City Learning Programme, this article reveals that a replication policy approach from the social innovation lenses might be enabled as a multidirectional, radial, dynamic, iterative, and democratic learning process, overcoming the given unidirectional, hierarchical, mechanistic, solutionist, and technocratic approach.

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Is measuring social innovation a mission impossible?

Cited by 21 publications

References 26 publications

A decision model approach for determining social innovation potential of technological projects

A decision model approach for determining social innovation potential of technological projects

Democratising Smart Cities? Penta-Helix Multistakeholder Social Innovation Framework

Replicating Smart Cities: The City-to-City Learning Programme in the Replicate EC-H2020-SCC Project

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