Crowdsourcing with mobile apps brings 'big data' to psychological research

Local governments are increasingly developing electronic participation initiatives, expecting citizen involvement in local community affairs. Our objective was to assess e-participation and the extent of its change in local government in Greece. Using content analysis for 325 Greek municipal websites, we assessed e-participation status in 2017 and 2018 and examined the impact of change between these years. The assessment regards two consecutive years since the adoption of digital technologies by municipalities has been rapid. The main findings show that Greek local governments have made significant small- to medium-scale changes, in order to engage citizens and local societies electronically. We conclude that the integration of advanced digital technologies in municipalities remains underdeveloped. We propose that Greek municipalities need to consider incorporating new technologies, such as mobile apps, social media and big data, as well as e-decision making processes, in order to eliminate those obstacles that hinder citizen engagement in local government. Moreover, the COVID-19 outbreak has highlighted the need for enhancing e-participation and policymakers’ coordination through advanced digital technologies.

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Big Data Ethics and Politics: Toward New Understandings

Social Science Computer Review ◽

10.1177/0894439318810734 ◽

2018 ◽

Vol 38 (1) ◽

pp. 3-9 ◽

Cited By ~ 1

Author(s):

Wenhong Chen ◽

Anabel Quan-Haase

Keyword(s):

Big Data ◽

Mobile Apps ◽

Data Ethics ◽

The Social ◽

Wide Range ◽

Health Institutions ◽

Methodological Approaches ◽

Privacy Breaches ◽

The Many ◽

Ethics And Politics

The hype around big data does not seem to abate nor do the scandals. Privacy breaches in the collection, use, and sharing of big data have affected all the major tech players, be it Facebook, Google, Apple, or Uber, and go beyond the corporate world including governments, municipalities, and educational and health institutions. What has come to light is that enabled by the rapid growth of social media and mobile apps, various stakeholders collect and use large amounts of data, disregarding the ethics and politics. As big data touch on many realms of daily life and have profound impacts in the social world, the scrutiny around big data practice becomes increasingly relevant. This special issue investigates the ethics and politics of big data using a wide range of theoretical and methodological approaches. Together, the articles provide new understandings of the many dimensions of big data ethics and politics, showing it is important to understand and increase awareness of the biases and limitations inherent in big data analysis and practices.

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Big Data In Psychological Research

Computational Social Psychology ◽

10.4324/9781315173726-15 ◽

2017 ◽

pp. 332-348

Author(s):

David Serfass ◽

Andrzej Nowak ◽

Ryne Sherman

Keyword(s):

Big Data ◽

Psychological Research

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Experiencias y retos del uso de datos de aplicaciones móviles para la movilidad urbana

Revista de Arquitectura ◽

10.14718/revarq.2020.3039 ◽

2019 ◽

Vol 22 (1) ◽

Author(s):

Anna-Beatriz Sabino ◽

Pedro Reis-Martins ◽

Mauricio Carranza-Infante

Keyword(s):

Big Data ◽

Local Governments ◽

Rio De Janeiro ◽

Smart Cities ◽

Mobile Apps ◽

Sao Paulo ◽

São Paulo ◽

Urban Mobility ◽

Simple Task ◽

Use Of Data

Resumen La planeación de la movilidad urbana requiere utilización de datos masivos para apoyar la toma de decisiones y realizar proyecciones estratégicas, es así como muchos gobiernos locales no poseen la capacidad para generar los datos necesarios. Sin embargo, empresas privadas como Waze Moovit, Stava y Uber (gestores de aplicativos móviles de movilidad) tienen la capacidad de producir estos datos y, además, han demostrado su disponibilidad para compartirlos y así mejorar las condiciones de la planeación de la movilidad en las ciudades. En América del Sur, Rio de Janeiro, Sao Paulo y Medellín, son casos de ciudades que se convirtieron en ejemplos de innovación en el de uso de datos. Con base en la experiencia de estas ciudades y en encuestas aplicadas con representantes de empresas gestoras de Apps de movilidad y de gobiernos, en este artículo se propone un modelo de tres niveles para el uso de datos en beneficio de la gestión y planeación de la movilidad urbana. El modelo propuesto tiene como objetivo trazar parámetros que ayuden a las ciudades a desarrollar una visión en cuanto al potencial de los datos para generar acciones y políticas públicas de movilidad urbana. Palabras clave: Apps de movilidad; Big Data; gestión de tráfico colaborativo; movilidad Inteligente; planificación del tránsito; planificación urbana; Smart Cities; transporte; Abstract Urban mobility planning is included in a global scenario of increasing use of massive data to support decision making. However, many local governments do not have a structure that produces the data necessary to base their strategic projections. At the same time, private companies - such as Waze and Moovit (mobile application mobility managers) - have the ability to produce this data and, in addition, have demonstrated their availability to share them and thus improve planning conditions in cities. Nevertheless, managing this data and using it for the benefit of better urban planning and management is not a simple task. In South America, Rio de Janeiro, Sao Paulo and Medellín have overcome important obstacles in this trajectory and became examples of innovation in the use of data. Based on the experience of these cities - and on surveys conducted with representatives of mobile apps companies and governments -, this article proposes a three-level model for the use of data for the benefit of urban mobility management and planning. The proposed model is in its initial stage and aims to draw parameters that help cities to develop a vision regarding the potential of data to generate actions and public policies of urban mobility. Keywords: Mobility apps; Big data; collaborative traffic management; Smart mobility; traffic planning; urban planification; transport; Smart Cities. Recibido: septiembre 9 / 2019 Evaluado: noviembre 30 / 2019 Aceptado: diciembre 18 / 2019 Publicado en línea: diciembre de 2019 Actualizado: diciembre de 2019

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Review on the Revolution of Psychology Research in the Age of Big Data

Journal of Psychological Research ◽

10.30564/jpr.v1i1.197 ◽

2018 ◽

Vol 1 (1) ◽

Author(s):

Guoxing Han

Keyword(s):

Big Data ◽

Research Methods ◽

Technological Development ◽

Current Psychological Research ◽

Psychological Research ◽

Psychology Research ◽

Science System ◽

Practical Applications ◽

Modern Information Technology ◽

The Impact

As a concept that came into being with the information age, big data hasreceived common attention from all walks of life in recent years, includingpsychology. The text introduces the concept of big data and its technical tools from a technical point of view, summarizes the research logic and research methods of traditional psychology, and introduces the big data problems in psychology research and some related practical applications. It summarizes the impact of big data on the research logic and research methods of psychology. The emergence of big data is an inevitable outcome of technological development. Psychology, as a subject of externalperformance data, should seize this opportunity. For many aspects of current psychological research, big data technology can directly improve efficiency and enhance validity. At the same time, if researchers start from the goal of psychological research and make full use of modern information technology, combining big data with psychology and psychology research paradigms. It is expected to expand the field and ideas of psychological research and promote the further development of the psychological science system.

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Influence of modern information technologies on the worldview

Social Psychology and Society ◽

10.17759/sps.2019100403 ◽

2019 ◽

Vol 10 (4) ◽

pp. 23-41 ◽

Cited By ~ 3

Author(s):

S.V. Krainyukov

Keyword(s):

Big Data ◽

Sentiment Analysis ◽

Corpus Linguistics ◽

Information Technologies ◽

Psychological Research ◽

High Potential ◽

Virtual Space ◽

Psychological Phenomenon ◽

Methodological Problems ◽

Modern Information

The article reveals and summarizes various approaches to the study of the influence of modern information technologies on the worldview. The importance of addressing the study of a holistic worldview as an integral and dynamic psychological phenomenon is justified. Particular attention is paid to the concepts of computer metaphor, sociocultural pathology and clip thinking. Education approaches are analyzed in this context. The problem of methods for studying the worldview of an individual and group subject in virtual space is discussed: methods for working with big data, sentiment analysis of the text, corpus linguistics methods. Some of the possibilities of these methods are illustrated, their advantages and limitations are considered, their high potential in psychological research is emphasized. Methodological problems of studying the influence of modern information technologies on the worldview are formulated, in particular, the uncertainty of terms in the framework of the concept of clip thinking, the paucity of differential psychological studies and psychological methods.

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The Big Data toolkit for Psychologists: Data Sources and Methodologies

10.31234/osf.io/keqmh ◽

2021 ◽

Author(s):

Heinrich Peters ◽

Zachariah Marrero ◽

Samuel D. Gosling

Keyword(s):

Big Data ◽

Data Storage ◽

Computation Time ◽

Psychological Research ◽

Data Sources ◽

Machine Learning Techniques ◽

Challenges And Opportunities ◽

Huge Impact ◽

Analytic Strategy ◽

The Right

As human interactions have shifted to virtual spaces and as sensing systems have become more affordable, an increasing share of peoples’ everyday lives can be captured in real time. The availability of such fine-grained behavioral data from billions of people has the potential to enable great leaps in our understanding of human behavior. However, such data also pose challenges to engineers and behavioral scientists alike, requiring a specialized set of tools and methodologies to generate psychologically relevant insights.In particular, researchers may need to utilize machine learning techniques to extract information from unstructured or semi-structured data, reduce high-dimensional data to a smaller number of variables, and efficiently deal with extremely large sample sizes. Such procedures can be computationally expensive, requiring researchers to balance computation time with processing power and memory capacity. Whereas modelling procedures on small datasets will usually take mere moments to execute, applying modeling procedures to big data can take much longer with typical execution times spanning hours, days, or even weeks depending on the complexity of the problem and the resources available. Seemingly subtle decisions regarding preprocessing and analytic strategy can end up having a huge impact on the viability of executing analyses within a reasonable timeframe. Consequently, researchers must anticipate potential pitfalls regarding the interplay of their analytic strategy with memory and computational constraints.Many researchers who are interested in using “big data” report having problems learning about new analytic methods or software, finding collaborators with the right skills and knowledge, and getting access to commercial or proprietary data for their research (Metzler et al. 2016). This chapter aims to serve as a practical introduction for psychologists who want to use large datasets and datasets from non-traditional data sources in their research (i.e., data not generated in the lab or through conventional surveys). First, we discuss the concept of big data and review some of the theoretical challenges and opportunities that arise with the availability of ever larger amounts of data. Second, we discuss practical implications and best practices with respect to data collection, data storage, data processing, and data modelling for psychological research in the age of big data.

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Achieving Resilience and Business Sustainability during COVID-19: The Role of Lean Supply Chain Practices and Digitalization

Sustainability ◽

10.3390/su132212369 ◽

2021 ◽

Vol 13 (22) ◽

pp. 12369

Author(s):

Matteo Trabucco ◽

Pietro De Giovanni

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Big Data ◽

Supply Chain ◽

Customer Service ◽

Mobile Apps ◽

Production Costs ◽

Sustainable Business ◽

Business Sustainability ◽

Logistic Regression Models

This paper investigates how firms can enjoy a sustainable business even during the COVID-19 pandemic. The adoption of lean coordination mechanisms over the supply chain (SC) and lean approaches in omnichannel strategies can guarantee the business sustainability and resilience. Furthermore, we investigate whether business sustainability, along with digitalization through mobile apps, Artificial Intelligence systems, and Big Data and Machine Learning enable firms’ resilience. We first explore the background on the subject, identify the research gap, and develop some research hypotheses to be tested. Then, we present the data collection process and the sample, which finally consists of firms from different sectors, including retailing, electronics, pharmaceutics, and agriculture. Several logistic regression models are developed and estimated to generate findings and managerial insights. Our results show that a lean omnichannel approach is an effective practice to preserve production costs, SC visibility, inventory available over the SC, and sales. Furthermore, lean coordination with contracts can make a business sustainable by preserving quality, ROI, production costs, customer service, and inventory availability. Finally, firms can be highly sustainable through resilience when they engage in sustainable ROI, SC visibility, and sales; in contrast, the adoption of mobile apps worsens firms’ resilience, which is not influenced by Artificial Intelligence and Big Data and Machine Learning.

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