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2 results for “Diffusion of Innovation”
Indirect social influence helps shaping the diffusion of innovations
<p>This dataset accompanies the paper "Indirect social influence helps shaping the diffusion of innovations".<br><br>It contains the participant choices between two colors when shown different information about the choices taken in their social network. The objective of the participants was to arrive to a consensus. One of the colors, which was the minority at the beggining, was given an extra incentive. Further details on the experimental setup are specified in the article.<br><br>The main file is "Unified_data", which contains all the data from the 21 sessions studied and whose fields are described below. The file "ParticipantsData" contains the gender and age distribution of the participants, while the rest of the files are the individual data for each session, in the format given by oTree.<br><br></p> <p>Explanation of "Unified_data.csv" fields:</p> <ul> <li>Experiment: Session number.</li> <li>participant.id_in_session: Identifier for each participant, constant for each session.</li> <li>config_name: Contains the order in which the different Setups were played. Its structure is of the form "egonetwork_1_a_b_c", where a,b,c are the numbers 2,3,4 in different order. These represent: 1-Setup AmAz (first neighbors), 2-Setup VerMag (first+second neighbors), 3-Setup RojNar (first+third neighbors), 4-Setup LilMor (first+fourth neighbors).</li> <li>Setup: Name of the Setup played (AmAz, VerMag, RojNar, LilMor and Instrucctions).</li> <li>node: Node assigned to the participant during that Setup.</li> <li>initialcolor: Initial color prescribed at the beginning of the Setup. Names are in spanish: Azul (blue) = #0072B2, Amarillo (yellow) = #FFC300, Verde (green) = #67E96E, Magenta = #C700AC, Rojo (red) = #E53950, Naranja (orange) = #FFA460, Lila (lilac) = #A792FC, Morado (purple) = #8000FF.</li> <li>InnovationAsInitialcolor: Binary variable showing if participant had the promoted color as initial color.</li> <li>threshold: Best threshold obtained in the model fitting.</li> <li>parameter: Best long-range parameter obtained in the model fitting.</li> <li>Time: Best time simulation obtained in the model fitting.</li> <li>AdoptersConsensus: First round in which all the participants chose the promoted color at least once.</li> <li>SimulationConsensus: Round in which the simulation of the fitted model arrived to consensus.</li> <li>Round: Round number.</li> <li>preg: Only in the Instructions Setup. Answer given to the control question.</li> <li>action: Color chosen by the participant in that round.</li> <li>Adopted_color: Previous varible translated into binary.</li> <li>Adopted_color_bin: Previous variable as logic variable.</li> <li>MajorityColor: Color most seen between the information given to the participant. In case of draw, one was chosen randomly.</li> <li>InnovationAsMajorityColorSeen: Binary variable showing if MajorityColor is equal to Promoted_color.</li> <li>bot: Binary variable showing if the participant was a bot in the current round (did not make a decision on time).</li> <li>color_neighbors_shown: List of the colors of the first neighbors shown to the participant in that round.</li> <li>color_neighbors_shown_color: Previous variable translated from hexadecimal to spanish.</li> <li>perc_color_neighbors: Percentage of neighbors shown with color equal to the promoted color.</li> <li>color_friends_shown: List of the colors of the long-distance friends shown to the participant in that round.</li> <li>color_friends_shown_color: Previous variable translated from hexadecimal to spanish.</li> <li>perc_color_friends: Percentage of long-distance friends shown with color equal to the promoted color.</li> <li>Adopted_innovation: Number of round since the participant first chose the promoted color.</li> <li>Promoted_color: Color with incentive. Setup AmAz - Azul, Setup VerMag - Magenta, Setup RojNar - Naranja, Setup LilMor - Morado.</li> <li>num_adopters: Number of adopters (people that have chosen the promoted color at least once) in the current round.</li> <li>total_participants: Constant showing the size of the network used (31).</li> <li>perc_adopters: Percentage of participants that are adopters in the current round.</li> </ul>
Interviewees and mapping of interview questions to areas of Innovation Diffusion Theory
<p>This submission includes two files complementing the F1000Research article "Preprints and Scholarly Communication: Adoption, Practices, Drivers and Barriers" - <a href="https://doi.org/10.12688/f1000research.19619.1">https://doi.org/10.12688/f1000research.19619.1</a></p> <p>The table '<strong>Extended data</strong>' shows how interview questions for our study were built based on the features of information diffusion theory (IDT) (Rogers, 2003). It should be noted that not all interview questions were asked to all stakeholder groups based on appropriateness and time available. Some questions in the table may appear to be repeated: this is because slightly different phrasing was used based on the stakeholder interviewed.</p> <p>Legend:</p> <ul> <li>Research Funder: RF</li> <li>Research Performing Organisation: RPO</li> <li>Preprint Server: PS</li> <li>Other Service Provider: OSP</li> <li>Engaged researcher: ER</li> <li>Unengaged researcher: UR</li> </ul> <p>The table '<strong>List of interviewees</strong>' includes all stakeholders interviewed in the context of this research. More information on this study is also available in the form of a <a href="http://doi.org/10.5281/zenodo.3357727">report</a>.</p>
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