1. Introduction
Creative and smart cities, streets and other public spaces are the most attractive urban spots that galvanize urban development through creative and smart economic processes and cultural identity. Public open spaces are crucial parts of all cities and exist in a great variety of types, forms and sizes (from squares, streets to playgrounds, parks, riverbanks and urban forests), each one providing different environmental and social services for all inhabitants. To increase the quality of urban life, public open spaces deserve priority attention, as they affect the townscape, provide ecological diversity, have relevance for healthy citizens and societal well-being and deliver important economic value. The list of benefits of public open spaces is long and strongly related to different types of spaces and their features in the urban fabric. Besides accessibility for all, they include a wide range of functions supporting the quality of environment, health and wellbeing, from ecosystem services to social cohesion sense of place and identity, possibility of choice and responsiveness to variety of needs related to everyday outdoor use and activities. The list of important aspects of public space includes the multiple stories and the body of narratives that are exercised on a place by users and the values they assign to it. These narratives and connections can be exploited to activate public open space and engage citizens in their improvements.
Another factor to consider is the relentless development of digital technology. In the last decades, ICT (information and communication technologies) and mobile devices have profoundly affected multiple aspects of our daily life—the way we work, learn and communicate with other, and how we spend our free time. The relationship between ICT and public open spaces (POS) is a growing challenge for ICT experts, spatial planners, social scientists and decision-makers [
1]. There are different examples of blended digital/public open spaces, e.g., digital displays in cities, Wi-Fi provision in parks and squares, on-the-spot tourist information, broadcasting and interactive art performances, urban games, etc. Several projects, activities and initiatives take up aspects of interaction among users, ICT and social behavior (e.g., CyberParks [
2], MobileCity [
3], Cyberbullying [
4], GreenKeys [
5]); others set up ICT systems for spatial analyses and planning methodologies (People Friendly Cities [
6]), as well as for a series of social networks and pervasive urban gaming. Participation of residents and participants of activities in the design process of public spaces of the city invite a more dynamic urban life. Another relevant aspect of ICT lies in their ability to enhance communication with (potential) users, transforming the production of public open spaces into an interactive process and enabling wide community participation and empowerment. Thus, ICT represent a valuable source of information that could be used in the production of a more responsive and inclusive urban environment. However, no past or ongoing projects or research initiatives tackle in a systematic approach to the involvement of different users in the production of ICT-enhanced public open spaces.
To address all these issues comprehensively, professional support is needed to structure the issues in a user-friendly way for non-professionals, enabling better-focused discussions and exchange for co-creation and help to develop practical solutions for real place and time implementation. The current research project analyses how ICT and spaces are used together and from there come up with ideas on how to provide this service in a more efficient way and more specifically tuned to the local context and different community members’ needs. The main goal of the paper is to present the design of Digital Co-Creation Index and to offer methodological guidelines for applying Digital Co-creation monitoring technique [
7] for evaluation of co-creation processes by designing attractive, inclusive and responsive public open spaces. The application procedure is illustrated with the evaluation results of four European Living Labs—Aukštamiestis Living Lab (Vilnius), Alvalade Living Lab (Lisbon), Città Studi Living Lab (Milano) and Zuid park Living Lab (Ghent). The Living Labs were selected due to their diversity, size, importance as active centers, availability and vibrant involvement of local communities during the implementation of C3Places project.
The paper is structured as follows.
Section 1 is the introductory section, in which the main idea of interaction between ICT and public open spaces is analyzed.
Section 2 focuses on methodology for designing indices for social phenomena.
Section 3 presents procedures applied in the development of digital co-creation assessment tool.
Section 4 presents methodological guidelines and results of experimental evaluation of four case studies through Digital Co-creation Index (DCCI) dimensions. Discussion and limitation are outlined in
Section 5 and conclusions in
Section 6.
3. Digital Co-Creation Index
As mentioned in the previous chapter, “the Index is a numerical value that expresses the statistical relationship between amounts relating to the same phenomenon. The numerical value is precisely what gives us an insight on the phenomenon we hope to analyze and measure” [
17]. The proposed Digital Co-creation Index (DCCI) methodology focuses on facilitating the framework to evaluate the co-creation processes for designing attractive, inclusive and responsive public open spaces (POS). The framework summarizes the current research progress on the topic and was developed as a part of C3Places project [
7]. According to the concept of the composite index introduced in
Section 2, DCCI was developed consisting of three Sub-Indices [
18]:
POS Quality Index evaluates the physical and social aspects of the observed public space that are forming its quality;
Digital Inclusiveness Index explains technological readiness of the initiative for enabling co-creation and measures preconditions for the inclusiveness of public places;
Social Responsiveness Index refers to the co-creative maturity of actors (stakeholders and community members) in responding to the social challenges and in generating the public value.
The index construction methodology is a constituent part of DCCI research methodology which fully complies with the system approach to the analyzed subject. On the basis of the composite index construction experience [
19], the following stages were distinguished in modelling the Digital Co-creation Index.
Theoretical review, construction of the conceptual model. The analysis of previous research efforts captured the theoretical influences and provided the basis for the selection of framework dimensions. The POS Quality dimension was developed in combining the Project for Public Places [
20] and Quality of Experience frameworks [
21], which identified four qualities determining its attractiveness: uses and activities; comfort and image; access and linkages; sociability by evaluating thousands of public spaces globally. The Social Networking Adoption Model [
22], which helps the public organizations to weigh the benefits and risks associated with the use of ICT and social networking applications, formed the base for Digital Inclusiveness pillar. Social Responsiveness dimension was adapted from the Collective Intelligence Index [
23]. The second step of the process was the expert interviews. The in-depth knowledge provided by the experts on the key evaluation points was particularly suited for broadening the theoretical framework. Nine purposively sampled semi-structured face-to-face expert interviews were conducted to check and improve the theoretical model.
Selection of evaluation criteria and proposal of assessment guidelines. The qualitative data collected during the interviews were analyzed in the context of respondents’ ideas, arguments and opinions in order to deepen the researchers’ understanding of the analyzed issues. At this stage the methods for data collection were chosen and described.
Collection of data in the Living Labs (Vilnius, Lisbon, Ghent, Milano). The experimental evaluation of Living Labs involved the use of a newly constructed measurement instrument. In the course of the experiment, the measurement scales were adjusted and improved. The values of the indicators are of a qualitative nature; therefore indicators underwent a qualitative evaluation and were ascribed numeric values that corresponded to their quantitative weight: 0, 0.5 or 1. All calculated indexes depend on the logic-categorical variables that determine the results of survey.
The values of answers to questions were transformed into a numeric scale in accordance with the following procedure (keeping the property of monotonicity of function and according to the intuitive reasoning). The function f, describing this procedure is defined by following approach: yes—1; no—0. Other categorical variables were transformed into a numeric scale applying the same approach: high—1; medium—0.5; low—0. To ascribe the numeric values, the variables underwent transformation f, which retained the intuitive order of the values of the categorical variables in the set of non-negative real numbers. To preserve measurability features, a set of non-negative numbers has been chosen. If the questions had no responses too often, their corresponding indicators were excluded from the index. If the interview failed to produce data only in several cases, the corresponding indicator was attributed the most frequently recurring value. Such attribution is sufficient for the purposes of the experiment as more complex cases were absent; usually, when frequently recurring numbers include several values, the problem of missing data is addressed by ascribing the missing position the arithmetic mean of the recurrent values.
Transformation f was also supplemented by rating of indicator values (since the values (and scales) are chosen from the range (0, 1)):
We assume that the weighted coefficients of each indicator inside each category is equal;
Ki is the estimate of weighted coefficient of i-th category, ;
is the transformed estimate of j-th indicator of i-th category using formula ;
mi is the number of variables (indicators) of i-th category;
n is the number of categories, defining the Digital Co-Creation Index.
The values of all three composite indices are identified by means of corresponding formulas specified further. Values of the indices fall into the range of real numbers (0, 1). To improve the user perception, the obtained values of the composite indices were transformed into a more attractive scale by multiplying the obtained values by, for example, 100. As the indices have just been introduced, any additional transformations are impossible, until they empirically prove to match the actual data. When the actual data and the values of their indices (or their evolution) differ essentially (in accordance to the corresponding criteria), changes of index defining formulas are necessary to lay down other leverage coefficients of the indicators (first type structural change) or include new indicators (second type structural change).
POS (public open space) Quality Index (POS QI) has 14 variables divided in four categories, which are used to define the attractiveness of open public space. We assumed that the categories were equally significant based on our theoretical insights and all variables used in these categories have the equal weight. POS Quality Index is calculated by applying the following formula for categories (also see
Table A1 in
Appendix A) (where AL—Access and linkage; CI—Comfort and image; UA—Uses and activities; S—Sociability):
The estimates of weighted coefficients of category are estimated by expert assessment. As no numeric data have been collected until the present experiment, there are no possibilities to carry out statistical research and identify statistical significance of each indicator necessary to construct the indexes. Therefore, leverage coefficients of the indicators (or categories) are determined in view of the acquired empirical experience in defining indicator correlation significance.
Digital Inclusiveness Index (DII) has, in total, seven exogenic variables, divided into five categories, which are used to determine the Digital inclusiveness Index (
Table A2 in
Appendix A). We assumed that categories are equally significant based on our theoretical insights and all variables used in these categories have equal weight. DII was calculated by applying the following formula for categories (where RRI—Risk related technologies; ERT—Expansion-related technologies; SVT—Social value creating technologies; PT—Pervasiveness of ICT; AT—Appropriateness of ICT):
The estimates of weighted coefficients of the category are estimated by expert assessment. As no numeric data have been collected until the present experiment, there are no possibilities to carry out statistical research and identify statistical significance of each indicator necessary to construct the indexes. Therefore, leverage coefficients of the indicators (or categories) were determined in view of the acquired empiric experience in defining indicator correlation significance.
Social Responsiveness Index (SRI). In total, 11 exogenic variables, divided into five categories, were used to determine the Social Responsiveness Index (
Table A3). We assumed that categories were equally significant based on our theoretical insights and all variables used in these categories have the equal weight. Thus, the SRI value is determined applying the formula as follows (where DOF—Dynamism, openness and flexibility; T—Transparency, applicable for the communities only; DS—Decentralization and self-organization, applicable for communities only; SI—Social impact and engagement; GPV—Generated public value):
The estimates of weighted coefficients of category are estimated by expert assessment. As no numeric data on the observed phenomena have been collected until the present experiment, it was not possible to carry out statistical research and identify statistical significance of each indicator necessary to construct the indexes. Therefore, leverage coefficients of the indicators (or categories) were determined in view of the acquired empirical experience in defining indicator correlation significance.
Digital Co-Creation Index (DCCI). The Digital Co-Creation Index is designed around three different indices: POS Quality Index, Digital Inclusiveness Index and Social Responsiveness Index. The Digital Co-Creation Index is numerical value that expresses the mean of these three Indices. The Digital Co-Creation Index formula is the following:
At the current stage of the research, we assume that three indices are equally significant.