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1

Rubenstein, David. "Black-Box Immigration Federalism." Michigan Law Review, no. 114.6 (2016): 983. http://dx.doi.org/10.36644/mlr.114.6.black-box.

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In Immigration Outside the Law, Hiroshi Motomura confronts the three hardest questions in immigration today: what to do about our undocumented population, who should decide, and by what legal process. Motomura’s treatment is characteristically visionary, analytically rich, and eminently fair to competing views. The book’s intellectual arc begins with its title: “Immigration Outside the Law.” As the narrative unfolds, however, Motomura explains that undocumented immigrants are “Americans in waiting,” with moral and legal claims to societal integration.
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Buchanan, Rowan Hisayo. "Black Box." Wasafiri 37, no. 3 (July 3, 2022): 110–11. http://dx.doi.org/10.1080/02690055.2022.2067300.

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Lukawetz, Gerhard. "Black Box." MedienJournal 20, no. 1 (May 5, 2017): 24–28. http://dx.doi.org/10.24989/medienjournal.v20i1.613.

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Selbst wenn computerbasierte Medien erst in jüngster Zeit öffentliche Beachtung gefunden haben, sind sie im wissenschaftlichen Bereich und unter technikbegeisterten Privatpersonen seit mehr als einem Jahrzehnt verbreitet. Mit der Computermailbox »Black•Box« kann auch Österreich auf ein bereits seit mehreren Jahren »etabliertes« System verweisen. Die Black• Box Wien (B•B) hat sich innerhalb von knapp vier Jahren von einem Jugend-FreeNet zu einem Public Access System mit den Schwerpunkten Stadtpolitik und Kultur entwickelt, das mit über 4.000 regelmäßigen Benutzern europaweit wahrscheinlich das zweitgrößte Non­Profit-Projekt (nach der »Digitalen Stadt Amsterdam«) darstellt.
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Kolbeck, Cornelia. "Black Box." kma - Klinik Management aktuell 12, no. 07 (July 2007): 14–16. http://dx.doi.org/10.1055/s-0036-1574278.

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Disease-Management-Programme gehören mittlerweile zur Versorgungswirklichkeit. Doch fünf Jahre nach deren Einführung liegen weder valide Aussagen zur medizinischen Ergebnisqualität noch zur Kosten-Nutzen-Bewertung vor. Mit der Reform des Risikostrukturausgleichs könnten sie noch profilloser werden.
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Clark, Lachlan, and Simon Bann. "Black box." BMJ 326, Suppl S3 (March 1, 2003): 030357. http://dx.doi.org/10.1136/sbmj.030357.

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6

Fitter, Alastair. "Black box or Pandora's box?" Trends in Ecology & Evolution 10, no. 2 (February 1995): 88–89. http://dx.doi.org/10.1016/s0169-5347(00)88991-0.

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Holzberger, Doris, Anja Philipp, and Mareike Kunter. "Ein Blick in die Black-Box." Zeitschrift für Entwicklungspsychologie und Pädagogische Psychologie 48, no. 2 (April 2016): 90–105. http://dx.doi.org/10.1026/0049-8637/a000150.

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Abstract. Zwar konnte bisherige Forschung bereits zeigen, dass intrinsische motivationale Orientierungen von Lehrkräften im Zusammenhang mit günstigen Ergebnisvariablen (z. B. Unterrichtsqualität oder Schülermotivation) stehen, mögliche Wirkmechanismen sind jedoch noch wenig erforscht. Ziel der vorliegenden Studie ist es, diese vermittelnden Prozesse am Beispiel des Unterrichtsenthusiasmus von Lehrkräften zu untersuchen. Es wird angenommen, dass angehende Lehrkräfte mit hohem Unterrichtsenthusiasmus quantitativ (höhere Intensität) und qualitativ (Nutzung von unterrichtsbezogenen Lerngelegenheiten) anderes Arbeitsverhalten zeigen, was zu besserem Unterricht führt. Die Variablen wurden bei 362 deutschen Lehramtsanwärter(inne)n in der Mitte ihres zweijährigen Vorbereitungsdienstes per Fragebogen erhoben. Latente Mediatoranalysen belegen eine Teilmediation durch die Intensität ebenso wie die Nutzung von Lerngelegenheiten auf kognitive Aktivierung und emotionale Aspekte des Unterrichtshandelns. Die Ergebnisse stellen einen ersten Hinweis auf mediierende Variablen im Zusammenhang zwischen intrinsischen motivationalen Orientierungen und Unterrichtshandeln dar.
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Rakholiya, Kalpesh R., and Dr Dhaval Kathiriya. "Efficient Black-Box Collision Search in cryptanalysis." Paripex - Indian Journal Of Research 2, no. 1 (January 15, 2012): 30–31. http://dx.doi.org/10.15373/22501991/jan2013/11.

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Tawfik, Abdel. "Black Box QGP." SOP Transactions on Theoretical Physics 2014, no. 1 (February 28, 2014): 7–13. http://dx.doi.org/10.15764/tphy.2014.01002.

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10

Wiseman, Jack. "The Black Box." Economic Journal 101, no. 404 (January 1991): 149. http://dx.doi.org/10.2307/2233850.

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11

Chiu, Arthur O., David Bayliss, and David A. Savitz. "Black Box Epidemiology." Epidemiology 6, no. 4 (July 1995): 464. http://dx.doi.org/10.1097/00001648-199507000-00032.

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Chiu, Arthur O., David Bayliss, and David A. Savitz. "Black Box Epidemiology." Epidemiology 6, no. 4 (July 1995): 464. http://dx.doi.org/10.1097/00001648-199507000-00033.

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13

Mejía-Aranguré, Juan Manuel, Arturo Fajardo-Gutiérrez, and Manuel Ortega-Alvarez. "“Black-Box” Epidemiology." Epidemiology 16, no. 3 (May 2005): 418. http://dx.doi.org/10.1097/01.ede.0000158791.23021.5b.

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Greenland, Sander, Manuela Gago-Dominguez, and Jose Esteban Castelao. "“Black-Box” Epidemiology." Epidemiology 16, no. 3 (May 2005): 419. http://dx.doi.org/10.1097/01.ede.0000158795.72215.e9.

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Neutra, Raymond Richard. "“Black-Box” Epidemiology." Epidemiology 16, no. 3 (May 2005): 418–19. http://dx.doi.org/10.1097/01.ede.0000158798.73608.44.

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16

Beckwith, Francis J., Michael Behe, and William A. Dembski. "Darwin's Black Box." Journal of Law and Religion 16, no. 2 (2001): 921. http://dx.doi.org/10.2307/1051754.

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17

Bodner, Donald R. "Black Box Unraveled." Journal of Spinal Cord Medicine 34, no. 1 (January 2011): 3. http://dx.doi.org/10.1179/1079026812z.000000000123.

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Uzman, Akif. "Darwin's Black Box." Biochemical Education 28, no. 3 (May 2000): 183. http://dx.doi.org/10.1016/s0307-4412(00)00010-8.

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19

Kampf, Günter. "Black Box Oxidizers." Infection Control & Hospital Epidemiology 38, no. 11 (September 26, 2017): 1387–88. http://dx.doi.org/10.1017/ice.2017.199.

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20

Nolan, Gerry. "Black box satellite." New Scientist 203, no. 2726 (September 2009): 27. http://dx.doi.org/10.1016/s0262-4079(09)62478-7.

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WILSON, ELIZABETH K. "BLACK-BOX CHEMISTRY." Chemical & Engineering News Archive 89, no. 33 (August 15, 2011): 36–37. http://dx.doi.org/10.1021/cen-v089n033.p036.

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22

Gent, Edd. "Big black box." New Scientist 240, no. 3199 (October 2018): 42–45. http://dx.doi.org/10.1016/s0262-4079(18)31850-5.

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23

Cavalier-Smith, Tom. "Darwin's Black Box." Trends in Ecology & Evolution 12, no. 4 (April 1997): 162–63. http://dx.doi.org/10.1016/s0169-5347(97)89790-x.

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Rezaeezadeh, Amin. "Magnetic black box." American Journal of Physics 78, no. 12 (December 2010): 1393–96. http://dx.doi.org/10.1119/1.3481104.

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25

Hensley, Roger. "Black-Box Warnings." Psychiatric News 41, no. 14 (July 21, 2006): 30. http://dx.doi.org/10.1176/pn.41.14.0030.

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26

Whittaker, James A., and Herbert H. Thompson. "Black Box Debugging." Queue 1, no. 9 (December 2003): 68–74. http://dx.doi.org/10.1145/966789.966807.

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27

Jackson, Sally. "Black Box Arguments." Argumentation 22, no. 3 (April 23, 2008): 437–46. http://dx.doi.org/10.1007/s10503-008-9094-y.

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28

Koppensteiner, Franz A. M. "Black Box EuGH?" Europarecht 57, no. 6 (2022): 711–30. http://dx.doi.org/10.5771/0531-2485-2022-6-711.

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Von außen wird der EuGH oftmals als Black Box gesehen. Vor diesem Hintergrund wird im folgenden Beitrag der Frage nachgegangen, wie transparent der EuGH tatsächlich ist; dabei werden unterschiedliche Verfahrensstadien untersucht und Verbesserungsvorschläge näher erörtert.
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29

GROSS, BRIANA L. "MADS-box out of the black box." Molecular Ecology 20, no. 1 (December 16, 2010): 25–26. http://dx.doi.org/10.1111/j.1365-294x.2010.04894.x.

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30

Kasianiuk, Krzysztof. "White box, black box and self-organization." Kybernetes 45, no. 1 (January 11, 2016): 126–40. http://dx.doi.org/10.1108/k-02-2015-0057.

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Purpose – The purpose of this paper is to present two system identification models – “white box” and “black box” – as useful tools that help understand self-organization processes within and outside the organizations facilitated by leaders. Every leader is presented as a “systems designer” who plays a fundamental role in the process of self-organization, both within and outside the organization under study. Design/methodology/approach – First, “white box” and “black box” system identification models are presented as a basis for an integrated model of the “system” and its “environment.” Next, the ideas of “closed” and “open” systems as the prerequisites of self-organization processes are described. Finally, two basic leadership tactics as well as their combination are characterized and discussed. Findings – Two system identification models give a complementary view to the reality, as they combine both reductionist and holistic perspectives. The argument presented in the paper shows that there is a far reaching complementarity of the two system identification models. Practical implications – Since leaders need to comprehend complex adaptive processes taking place in the organizations and in their environment, they search for the best strategy to approach this task. The tactics presented in the paper could serve as a cognitive tools that help approach the reality leaders are immersed in. Originality/value – The paper utilizes two categories that are well recognized in systems theory and cybernetics, combines them with the idea of self-organization and puts it all in the context of leadership. It provides an integrated, yet relatively simple cognitive scheme that may be of theoretical and practical use.
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31

Leshkevich, Tatiana G. "Metaphors of the digital age and the Black Box Problem." Philosophy of Science and Technology 27, no. 1 (2022): 34–48. http://dx.doi.org/10.21146/2413-9084-2022-27-1-34-48.

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The article discusses the effects of the digital era, the driver of which is AI. The main goal is to fo­cus on the Black Box Problem, “opacity of AI” and the possibility of Malicious Use of Artificial In­telligence. Three interconnected directions are interfaced. Firstly, in the context of the analysis of the digital age, the potential of metaphors is used, which makes it possible to describe digital trans­formations figuratively. Secondly, due to the growing demand for high technologies, the negative consequences of using AI are considered and a number of paradoxes of scientific and technological progress are formulated. Thirdly, the article examines the widespread practice of trust in intelligent systems, as well as the prospects for technological symbiosis. The analysis is based on the Russian and English-language literature. The author analyzes metaphors that indicate the type of modern existence – “face-to-screen” or “face-to-device” (1); features of the subject of the digital age – “content viewer” (2); the specifics of digital rationality – “knowledge rent”, digital multitasking (3). Attention is drawn to the process of “convergence of subjectivity”. The issue of malicious use of AI is discussed. The author draws conclusions about the need for “algorithmic responsibility” and expanding the field of reflective analysis aimed at studying the consequences of using AI.
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32

White, Ruffin, Gianluca Caiazza, Agostino Cortesi, Young Im Cho, and Henrik I. Christensen. "Black Block Recorder: Immutable Black Box Logging for Robots via Blockchain." IEEE Robotics and Automation Letters 4, no. 4 (October 2019): 3812–19. http://dx.doi.org/10.1109/lra.2019.2928780.

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33

Kaspar, Wendi Arant. "Opening the Black Box." College & Research Libraries 77, no. 5 (September 1, 2016): 564–67. http://dx.doi.org/10.5860/crl.77.5.564.

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34

Rudnick, Abraham. "The Black Box Myth." International Journal of Extreme Automation and Connectivity in Healthcare 1, no. 1 (January 2019): 1–3. http://dx.doi.org/10.4018/ijeach.2019010101.

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Artificial intelligence (AI) and its correlates, such as machine and deep learning, are changing health care, where complex matters such as comoribidity call for dynamic decision-making. Yet, some people argue for extreme caution, referring to AI and its correlates as a black box. This brief article uses philosophy and science to address the black box argument about knowledge as a myth, concluding that this argument is misleading as it ignores a fundamental tenet of science, i.e., that no empirical knowledge is certain, and that scientific facts – as well as methods – often change. Instead, control of the technology of AI and its correlates has to be addressed to mitigate such unexpected negative consequences.
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35

Trammell, Susan. "Black in the Box." CFA Institute Magazine 15, no. 6 (November 2004): 26–29. http://dx.doi.org/10.2469/cfm.v15.n6.2895.

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Eltabakh, Mohamed, Anantha Subramanian, Awny Al-Omari, Mohammed Al-Kateb, Sanjay Nair, Mahbub Hasan, Wellington Cabrera, Charles Zhang, Amit Kishore, and Snigdha Prasad. "Not black-box anymore!" Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 2959–71. http://dx.doi.org/10.14778/3476311.3476375.

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Teradata Vantage is a platform for integrating a broad range of analytical functions and capabilities with the Teradata's SQL engine. One of the main challenges in optimizing the execution of these analytical functions is that many of them are not only black boxes, but also have polymorphic nature, i.e., their behavior and properties may change depending on the invocation context. In this paper, we first demonstrate the inherent complexity in optimizing polymorphic functions, and then present the Vantage's Collaborative Optimizer , which is a cross-platform optimizer designed for optimizing the analytical functions invoked from within the SQL engine. The Collaborative Optimizer is the industry-first effort towards enabling analytics-aware optimizations over polymorphic analytical functions. We present a novel markup language-based approach for expressing the functions' polymorphic properties via a set of well-defined instructions. The Collaborative Optimizer uses these instructions at query time to infer the corresponding properties, and then decide on the applicable optimizations. From several possible optimizations, we showcase two core optimizations, namely "projection push" and "predicate push" , which aim at optimizing the data movement to and from the analytical functions. The experiments using the Teradata-MLE analytical system demonstrate the expressiveness power and flexibility of the proposed markup language. Moreover, benchmark and real-world customer queries show the significant performance gain that the Collaborative Optimizer brings to the Vantage system.
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37

Price, W. "Regulating Black-Box Medicine." Michigan Law Review, no. 116.3 (2017): 421. http://dx.doi.org/10.36644/mlr.116.3.regulating.

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Data drive modern medicine. And our tools to analyze those data are growing ever more powerful. As health data are collected in greater and greater amounts, sophisticated algorithms based on those data can drive medical innovation, improve the process of care, and increase efficiency. Those algorithms, however, vary widely in quality. Some are accurate and powerful, while others may be riddled with errors or based on faulty science. When an opaque algorithm recommends an insulin dose to a diabetic patient, how do we know that dose is correct? Patients, providers, and insurers face substantial difficulties in identifying high-quality algorithms; they lack both expertise and proprietary information. How should we ensure that medical algorithms are safe and effective? Medical algorithms need regulatory oversight, but that oversight must be appropriately tailored. Unfortunately, the Food and Drug Administration (FDA) has suggested that it will regulate algorithms under its traditional framework, a relatively rigid system that is likely to stifle innovation and to block the development of more flexible, current algorithms. This Article draws upon ideas from the new governance movement to suggest a different path. FDA should pursue a more adaptive regulatory approach with requirements that developers disclose information underlying their algorithms. Disclosure would allow FDA oversight to be supplemented with evaluation by providers, hospitals, and insurers. This collaborative approach would supplement the agency’s review with ongoing real-world feedback from sophisticated market actors. Medical algorithms have tremendous potential, but ensuring that such potential is developed in high-quality ways demands a careful balancing between public and private oversight, and a role for FDA that mediates—but does not dominate—the rapidly developing industry.
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38

Seeley, Schuyler D. "Dormancy—The Black Box." HortScience 29, no. 11 (November 1994): 1248. http://dx.doi.org/10.21273/hortsci.29.11.1248.

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39

Resende, David Nunes, Xiaowen Yu, Sílvia Gaia, and David V. Gibson. "Inside the Black Box." International Journal of Information Systems and Social Change 8, no. 4 (October 2017): 1–22. http://dx.doi.org/10.4018/ijissc.2017100101.

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This article uses an original three-phase approach for empirical assessment and comparative evaluation of the efficiency of university technology transfer. It is based on analysis of inputs and outputs of a disclosure phase followed by a value-add phase and a final license phase, using a multidimensional framework. The objective is to find university Technology Transfer Office (TTO) efficiency and effectiveness patterns for each phase as well as overall TT processes. A network Data Envelopment Analysis (DEA) model was used to analyze and describe the complicated TT operational processes using Association of University Technology Managers data collected from 90 US university TTOs for the period 2007-2013. It was concluded that the sampled TTOs were most inefficient in their value-add (2nd) phase and that the average overall efficiency as well as the efficiency of disclosure (1st) and license (3rd) phases decreased during the last five years of data analyzed. In addition, in line with other studies, analysis supports the contention that the presence of medical school does not increase TT efficiency or effectiveness. The present research's contributions focus on three areas: (1) Analysing and modelling TTO valorization and commercialization process with a UML activity diagram to provide a clear picture of TT procedures and processes; (2) Proposing a three-phase DEA framework showing input/output indicators closely related to each phase of processes rather than a black box or separated activities; (3) Offering a strategy to conduct empirical studies on TTO's operational efficiency thereby helping to better understand future research operational problems.
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40

Balaban, Avraham. "Amos Oz's Black Box." Revue belge de philologie et d'histoire 70, no. 3 (1992): 624–40. http://dx.doi.org/10.3406/rbph.1992.3835.

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41

Wackerhage, Henning, and Philip Atherton. "Inside the ‘black box’." Physiology News, Autumn 2004 (September 1, 2004): 11–12. http://dx.doi.org/10.36866/pn.56.11.

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42

Levy, Roger. "The Black Box Problem." RUSI Journal 164, no. 5-6 (September 19, 2019): 82–87. http://dx.doi.org/10.1080/03071847.2019.1694266.

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43

Whitcher, Ursula, Jeroen Demeyer, and William Stein. "Beyond the Black Box." Notices of the American Mathematical Society 63, no. 08 (September 1, 2016): 928–29. http://dx.doi.org/10.1090/noti1408.

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44

Asiaei, Kaveh, Omid Barani, Nick Bontis, and Maryam Arabahmadi. "Unpacking the black box." Journal of Intellectual Capital 21, no. 6 (March 17, 2020): 809–34. http://dx.doi.org/10.1108/jic-06-2019-0147.

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PurposeDrawing largely upon resource orchestration theory, this study aims to contribute to the intellectual capital (IC) literature by testing a model where intrapreneurship mobilizes resources to trigger firm performance. More specifically, this study investigates how intrapreneurship mediates the relationship between IC and financial performance.Design/methodology/approachData was collected using a structured questionnaire administered to a target sample of publicly-listed Iranian companies across a variety of sectors. Archival data supplemented the survey findings to capture financial performance. A structural equation modelling (SEM) approach, using LISREL, was used to assess the measurement and structural models.FindingsThe results supported the hypothesized associations among IC, intrapreneurship, and financial performance. Furthermore, the findings provided some evidence that IC is indirectly related to financial performance through the mediating role of intrapreneurship.Research limitations/implicationsThe focus on Iranian publicly listed companies limits the generalizability of results.Practical implicationsManagers need to align the company's strategic resources with other competencies such as intrapreneurial initiatives. The synthesis of knowledge resources and intrapreneurship can help organization to better organize, synchronize and support – i.e. “orchestrate” – their human and structural capital, improving the firm's social and innovation capital and eventually enhancing overall performance.Originality/valueTo our knowledge, this is the first study ever to explore the mediating role of intrapreneurship in the relationship between IC and financial performance from the resource orchestration lens.
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Weed, D. L. "Beyond black box epidemiology." American Journal of Public Health 88, no. 1 (January 1998): 12–14. http://dx.doi.org/10.2105/ajph.88.1.12.

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Mullen, Philomena. "Black baby box[ed]." Irish University Review 50, no. 2 (November 2020): 252–55. http://dx.doi.org/10.3366/iur.2020.0463.

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Alpert, Mark. "A Better Black Box." Scientific American 283, no. 3 (September 2000): 96–97. http://dx.doi.org/10.1038/scientificamerican0900-96.

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48

Flyverbom, Mikkel. "Beyond The Black Box." Social Epistemology 19, no. 2-3 (January 2005): 225–29. http://dx.doi.org/10.1080/02691720500145423.

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Rensvold, Sandy L. "Elizabethʼs Little Black Box." Journal of Christian Nursing 18, no. 2 (2001): 26–29. http://dx.doi.org/10.1097/00005217-200118020-00009.

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50

Gold, Christian. "Inside the black box." Nordic Journal of Music Therapy 26, no. 5 (August 4, 2017): 393–94. http://dx.doi.org/10.1080/08098131.2017.1351701.

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