Stereotyping Effects on Cities: Measurement Scales for City's Warmth and Competence

• Main Authors: Adriana ZAIŢ, Andreia Gabriela ANDREI, Ioana-Alexandra HORODNIC
• Format: Article
• Language: English
• Published: Faculty of Management National University of Political Studies and Public Administration 2016-06-01
• Series: Management Dynamics in the Knowledge Economy
• Subjects: warmth; competence; city competitiveness; smart city; place branding; scale validation; Q-sorting
• Online Access: http://www.managementdynamics.ro/index.php/journal/article/view/177/126

In the endeavor of analyzing urban development perspectives, the current paper aims to find out how warmth and competence stereotypes would operate in the case of a city, predicting its future, as a direct consequence of people's positive or negative feelings and actions. Results of such analyses would be of strategic importance, knowing that various aspects of urban development (from tourism to business, well-being, active population growth and talents retention) depend on people’s decisions to visit that city, to invest, to work, to study, to settle down there, or to simply spread positive opinions about it. Therefore, relying on the well-known SCM -stereotype content model, the paper adapts previous warmth and competence scales, and develops a customized research instrument for analyzing connections between people's perceptions and the mental labels attached to a specific city. Considering warmth and competence dimensions, as well as the other variables of interest such as status, cooperation and competition, we use an exploratory procedure for item selection followed by a Q-sorting analysis for scale content validation. The paper adds to the literature in two main ways. It firstly advances an integrative view that connects the theories from social psychology, communication and branding with those from urban development. Secondly, it offers a content validated measurement instrument, as a necessary departure point for future analyses meant to identify challenges and to predict the potential for development of smart and sustainable cities.