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Drawing on the concept of a gale of creative destruction in a capitalistic economy, we argue that initiatives to assess the robustness of findings in the organizational literature should aim to simultaneously test competing ideas operating in the same theoretical space. In other words, replication efforts should seek not just to support or question the original findings, but also to replace them with revised, stronger theories with greater explanatory power. Achieving this will typically require adding new measures, conditions, and subject populations to research designs, in order to carry out conceptual tests of multiple theories in addition to directly replicating the original findings. To illustrate the value of the creative destruction approach for theory pruning in organizational scholarship, we describe recent replication initiatives re-examining culture and work morality, working parents’ reasoning about day care options, and gender discrimination in hiring decisions.
Significance statement
It is becoming increasingly clear that many, if not most, published research findings across scientific fields are not readily replicable when the same method is repeated. Although extremely valuable, failed replications risk leaving a theoretical void— reducing confidence the original theoretical prediction is true, but not replacing it with positive evidence in favor of an alternative theory. We introduce the creative destruction approach to replication, which combines theory pruning methods from the field of management with emerging best practices from the open science movement, with the aim of making replications as generative as possible. In effect, we advocate for a Replication 2.0 movement in which the goal shifts from checking on the reliability of past findings to actively engaging in competitive theory testing and theory building.
Scientific transparency statement
The materials, code, and data for this article are posted publicly on the Open Science Framework, with links provided in the article.
Creative Destruction in Science
Warren Tierney;Jay Hardy;Charles R. Ebersole;Keith Leavitt;Domenico Viganola;Elena Giulia Clemente;Michael Gordon;Anna Dreber;Magnus Johannesson;Thomas Pfeiffer;Eric Luis Uhlmann
;Ajay T. Abraham;Matus Adamkovic;Jais Adam-Troian;Rahul Anand;Kelly J. Arbeau;Eli C. Awtrey;Ofer H. Azar;Štěpán Bahník;Gabriel Baník;Ana Barbosa Mendes;Michael M. Barger;Ernest Baskin;Jozef Bavolar;Ruud M. W. J. Berkers;Randy Besco;Michał Białek;Michael M. Bishop;Helena Bonache;Sabah Boufkhed;Mark J. Brandt;Max E. Butterfield;Nick Byrd;Neil R. Caton;Michelle L. Ceynar;Mike Corcoran;Thomas H. Costello;Leslie D. Cramblet Alvarez;Jamie Cummins;Oliver S. Curry;David P. Daniels;Lea L. Daskalo;Liora Daum-Avital;Martin V. Day;Matthew D. Deeg;Tara C. Dennehy;Erik Dietl;Eugen Dimant;Artur Domurat;Christilene du Plessis;Dmitrii Dubrov;Mahmoud M. Elsherif;Yuval Engel;Martin R. Fellenz;Sarahanne M. Field;Mustafa Firat;Raquel M. K. Freitag;Enav Friedmann;Omid Ghasemi;Matthew H. Goldberg;Amélie Gourdon-Kanhukamwe;Lorenz Graf-Vlachy;Jennifer A. Griffith;Dmitry Grigoryev;Sebastian Hafenbrädl;David Hagmann;Andrew H. Hales;Hyemin Han;Jason L. Harman;Andree Hartanto;Benjamin C. Holding;Astrid Hopfensitz;Joachim Hüffmeier;Jeffrey R. Huntsinger;Katarzyna Idzikowska;Åse H. Innes-Ker;Bastian Jaeger;Kristin Jankowsky;Shoshana N. Jarvis;Nilotpal Jha;David Jimenez-Gomez;Daniel Jolles;Bibiana Jozefiakova;Pavol Kačmár;Mariska Kappmeier;Matthias Kasper;Lucas Keller;Viktorija Knapic;Mikael Knutsson;Olga Kombeiz;Marta Kowal;Goedele Krekels;Tei Laine;Daniel Lakens;Bingjie Li;Ronda F. Lo;Jonas Ludwig;James C. Marcus;Melvin S. Marsh;Mario Martinoli;Marcel Martončik;Allison Master;Theodore C. Masters-Waage;Lewend Mayiwar;Jens Mazei;Randy J. McCarthy;Gemma S. McCarthy;Stephanie Mertens;Leticia Micheli;Marta Miklikowska;Talya Miron-Shatz;Andres Montealegre;David Moreau;Carmen Moret-Tatay;Marcello Negrini;Philip W. S. Newall;Gustav Nilsonne;Paweł Niszczota;Nurit Nobel;Aoife O'Mahony;Mehmet A. Orhan;Deirdre O'Shea;Flora E. Oswald;Miriam Panning;Peter C. Pantelis;Mariola Paruzel-Czachura;Mogens Jin Pedersen;Gordon Pennycook;Ori Plonsky;Vince Polito;Paul C. Price;Maximilian A. Primbs;John Protzko;Michael Quayle;Rima-Maria Rahal;Md. Shahinoor Rahman;Liz Redford;Niv Reggev;Caleb J. Reynolds;Marta Roczniewska;Ivan Ropovik;Robert M. Ross;Thomas J. Roulet;Andrea May Rowe;Silvia Saccardo;Margaret Samahita;Michael Schaerer;Joyce Elena Schleu;Brendan A. Schuetze;Ulrike Senftleben;Raffaello Seri;Zeev Shtudiner;Jack Shuai;Ray Sin;Varsha Singh;Aneeha Singh;Tatiana Sokolova;Victoria Song;Tom Stafford;Natalia Stanulewicz;Samantha M. Stevens;Eirik Strømland;Samantha Stronge;Kevin P. Sweeney;David Tannenbaum;Stephanie J. Tepper;Kian Siong Tey;Hsuchi Ting;Ian W. Tingen;Ana Todorovic;Hannah M. Y. Tse;Joshua M. Tybur;Gerald H. Vineyard;Alisa Voslinsky;Marek A. Vranka;Jonathan Wai;Alexander C. Walker;Laura E. Wallace;Tianlin Wang;Johanna M. Werz;Jan K. Woike;Conny E. Wollbrant;Joshua D. Wright;Sherry J. Wu;Qinyu Xiao;Paolo Barretto Yaranon;Siu Kit Yeung;Sangsuk Yoon;Karen Yu;Meltem Yucel
2020-01-01
Abstract
Drawing on the concept of a gale of creative destruction in a capitalistic economy, we argue that initiatives to assess the robustness of findings in the organizational literature should aim to simultaneously test competing ideas operating in the same theoretical space. In other words, replication efforts should seek not just to support or question the original findings, but also to replace them with revised, stronger theories with greater explanatory power. Achieving this will typically require adding new measures, conditions, and subject populations to research designs, in order to carry out conceptual tests of multiple theories in addition to directly replicating the original findings. To illustrate the value of the creative destruction approach for theory pruning in organizational scholarship, we describe recent replication initiatives re-examining culture and work morality, working parents’ reasoning about day care options, and gender discrimination in hiring decisions.
Significance statement
It is becoming increasingly clear that many, if not most, published research findings across scientific fields are not readily replicable when the same method is repeated. Although extremely valuable, failed replications risk leaving a theoretical void— reducing confidence the original theoretical prediction is true, but not replacing it with positive evidence in favor of an alternative theory. We introduce the creative destruction approach to replication, which combines theory pruning methods from the field of management with emerging best practices from the open science movement, with the aim of making replications as generative as possible. In effect, we advocate for a Replication 2.0 movement in which the goal shifts from checking on the reliability of past findings to actively engaging in competitive theory testing and theory building.
Scientific transparency statement
The materials, code, and data for this article are posted publicly on the Open Science Framework, with links provided in the article.
Theory pruning
Theory testing
Direct replication
Conceptual replication
Falsification
Hiring decisions
Gender discrimination
Work-family conflict
Cultural differences
Work values
Protestant work ethic
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2095744
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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 589/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.