S
Shruti Tewari
Researcher at Indian Institute of Management Indore
Publications - 26
Citations - 730
Shruti Tewari is an academic researcher from Indian Institute of Management Indore. The author has contributed to research in topics: Social identity theory & Medicine. The author has an hindex of 10, co-authored 19 publications receiving 464 citations. Previous affiliations of Shruti Tewari include Allahabad University & Indian Institute of Management Ahmedabad.
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Journal ArticleDOI
How Collective Participation Impacts Social Identity : A Longitudinal Study from India
Sammyh S. Khan,Nick Hopkins,Stephen Reicher,Shruti Tewari,Narayanan Srinivasan,Clifford Stevenson +5 more
TL;DR: In this article, a longitudinal questionnaire study conducted at one of the world's largest collective events (the Magh Mela) showed that participants who participated exhibited heightened social identification as a Hindu and increased frequency of prayer rituals.
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A calming cacophony: Social identity can shape the experience of loud noise
TL;DR: In this paper, the authors study the aural experience of a religious festival in North India which is characterized by loud, continuous and cacophonous noise and show that loud noise is experienced as pleasant or unpleasant according to the meanings attributed to it.
Journal ArticleDOI
Efficacy and well-being in rural north India: The role of social identification with a large-scale community identity.
Sammyh S. Khan,Nick Hopkins,Shruti Tewari,Narayanan Srinivasan,Stephen Reicher,Gozde Ozakinci +5 more
TL;DR: Social identification as a Hindu had positive and indirect associations with three measures of well-being through the belief that one can cope with everyday stressors and the application of social psychological theory developed in the urban West to rural north India is discussed.
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Social relations in crowds : recognition, validation and solidarity
TL;DR: In this article, the authors investigated the social-relational changes within a crowd and how these impact collective experience positively, using interviews with participants attending the annual Magh Mela pilgrimage in India.
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Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning
Tomislav Pavlović,Flavio Azevedo,Koustav De,Julián Riaño-Moreno,Marina Maglić,Theofilos Gkinopoulos,Patricio Andres Donnelly-Kehoe,César Payán-Gómez,Guanxiong Huang,Jaroslaw Kantorowicz,Michèle D. Birtel,Philipp Schönegger,Valerio Capraro,Hernando Santamaría-García,Meltem Yucel,Agustín Ibáñez,Steve Rathje,Erik Wetter,Dragana Stanojević,Jan-Willem van Prooijen,Eugenia Hesse,Christian T. Elbaek,Renata Franc,Zoran Pavlović,Panagiotis Mitkidis,Aleksandra Cichocka,Michele J. Gelfand,Mark Alfano,Robert Ross,Hallgeir Sjåstad,John B. Nezlek,Aleksandra Cislak,Patricia L. Lockwood,Koen Abts,Elena Agadullina,David M. Amodio,Matthew A. J. Apps,John Jamir Benzon R. Aruta,Sahba Besharati,Alexander Bor,Becky L. Choma,William A. Cunningham,Waqas Ejaz,Harry Farmer,Andres Findor,Biljana Gjoneska,Estrella Gualda,Toan Luu Duc Huynh,Mostak Ahamed Imran,Jacob Israelashvili,Elena Kantorowicz-Reznichenko,André Krouwel,Yordan Kutiyski,Michael Laakasuo,Claus Lamm,Jonathan Levy,Caroline Leygue,Min-Jen Lin,Mohammad S. Mansoor,Antoine Marie,Lewend Mayiwar,Honorata Mazepus,Cillian McHugh,Andreas Olsson,Tobias Otterbring,Dominic J. Packer,Jussi Palomäki,Anat Perry,Michael B. Petersen,Arathy Puthillam,Tobias Rothmund,Petra C. Schmid,David Stadelmann,Augustin Stoica,Drozdstoy Stoyanov,K. Stoyanova,Shruti Tewari,Bojan Todosijević,Benno Torgler,Manos Tsakiris,Hans H. Tung,Radu Umbreș,Edmunds Vanags,Madalina Vlasceanu,Andrew J. Vonasch,Yucheng Zhang,M. Viñarás Abad,Eli R. Adler,Hamza Alaoui Mdarhri,Benedict G. Antazo,F. C. Ay,Mouhamadou El Hady Ba,S. Barbosa,Brock Bastian,Anton Van den Berg,Michał Białek,Ennio Bilancini,N.I. Bogatyreva,Leonardo Boncinelli,Jonathan E. Booth,Sylvie Borau,Ondrej Buchel,Chrissie Ferreira de Carvalho,Tatiana Celadin,Chiara Cerami,Hom Nath Chalise,Xiaojun Cheng,Luca Cian,Kate Cockcroft,Jane Conway,M. Córdoba-Delgado,Chiara Crespi,Marie Crouzevialle,Jo Cutler,Marzena Cypryańska,Justyna Dąbrowska,Victoria H. Davis,John Paul Minda,Pamala N Dayley,Sylvain Delouvée,Ognjan Denkovski,Guillaume Dezecache,Nathan A. Dhaliwal,Alelie B Diato,Roberto Di Paolo,Uwe Dulleck,Jānis Ekmanis,Tom Etienne,Hapsa Hossain Farhana,Fahima Farkhari,Kristijan Fidanovski,Terry Flew,Shona Fraser,Raymond Boadi Frempong,Jonathan A. Fugelsang,Jessica Gale,E. Begoña García-Navarro,Prasad Garladinne,Kurt Gray,Siobhán M. Griffin,Bjarki Gronfeldt,June Gruber,Eran Halperin,Volo Herzon,Martin Hruska,Matthias F. C. Hudecek,Ozan Isler,Simon Jangard,F. Jørgensen,Oleksandra Keudel,Lina Koppel,Mika Koverola,Anton Johannes Olavi Kunnari,Josh Leota,Eva Lermer,C. Li,Chiara Longoni,Darragh McCashin,Igor Mikloušić,J. Molina-Paredes,César Monroy-Fonseca,Elena Morales-Marente,David Moreau,Rafał Muda,Annalisa Myer,Kyle Nash,Jonas P. Nitschke,Matthew S. Nurse,Victoria Oldemburgo de Mello,María Soledad Palacios-Gálvez,Yafeng Pan,Zsófia Márta Papp,Philip Pärnamets,Mariola Paruzel-Czachura,Silva Perander,Michael M. Pitman,Ali Raza,Gabriel Gaudencio Rêgo,Claire Robertson,Iván Rodríguez-Pascual,Teemu Juhani Saikkonen,Octavio Salvador-Ginez,Waldir Monteiro Sampaio,Gaia Chiara Santi,David T. Schultner,Enid Schutte,A. .Scott,Ahmed Skali,Anna Stefaniak,Anni Sternisko,Brent Strickland,Jeffrey Thomas,Gustav Tinghög,Iris J Traast,Raffaele Tucciarelli,Michael Tyrala,Nick D. Ungson,Mete Sefa Uysal,D. Van Rooy,Daniel Västfjäll,Joana Vieira,Christian von Sikorski,Alexander C. Walker,Jennifer Watermeyer,Robin Willardt,Michael J. A. Wohl,Adrian Dominik Wojcik,Kaidi Wu,Yuki Yamada,Onurcan Yilmaz,Kumar Yogeeswaran,Carolin-T. Ziemer,Rolf A. Zwaan,Paulo S. Boggio,Ashley V. Whillans,Paul A. M. Van Lange,Rajib Prasad,Michal Onderco,Cathal O'Madagain,Tarik Nesh-Nash,Oscar Laguna,Emily Kubin,Mert Gümren,A. N. Fenwick,Arhan Ertan,Michael J. Bernstein,Hanane Amara,Jay J. Van Bavel +227 more
TL;DR: This paper applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic.