Conceptual Foundations and an Organising Framework
Bibliographic record
Abstract
Organizations face institutional complexity whenever they confront incompatible prescripts from multiple institutional logics…. To the extent that the prescriptions and proscriptions are incompatible, or at least appear to be so, they invariably generate challenges and tensions for organizations exposed to them. —Greenwood et al. (2011: 318, italics in original) Institutions and institutional change have attracted considerable scholarly interest in numerous disciplines given their primacy as rules of the game in society that structure exchange between various societal actors (for example, Greif 2006; North 1990; Scott 2014). Research has scrutinised the process of institutional change, whether it is narrow or broad in scope, incremental or discontinuous, and exogenously or endogenously determined, among other characteristics (for example, Campbell 2004; Mahoney and Thelan 2010). There is a general agreement among scholars that irrespective of the process, institutional change encompasses shifting the rules of the game and imposes institutional complexity on the actors. Institutional changes emanating from evolving political and economic landscapes within individual countries and pressures from supranational bodies such as the World Trade Organization (WTO), the International Monetary Fund (IMF) and the World Bank have been instrumental in triggering economic reforms and liberalisation programmes of developing economies and their integration into the global economy (Gereffi 2010). Increasing integration into the global economy has transformed the competitive landscapes for developing country firms, thus necessitating organisational transformations to deal with new competitive dynamics. In the context of a variety of local and global institutional reforms, understanding how indigenous firms in developing economies worldwide respond to challenges presented by a radically changed competitive environment has been the subject of vigorous research in the past two decades (for example, Aulakh and Kotabe 2018; Newman 2000; Malerba and Lee 2021; Peng 2003; Uhlenbruck, Meyer and Hitt 2003; Zahra et al. 2000). The objective of this chapter is to use this body of research and its underlying theoretical approaches to develop an analytical framework through which the global institutional changes of interest in this study and the multilevel national responses (at the level of the state, industries and organisations) in the Indian textile and pharmaceutical industries can be evaluated in subsequent chapters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".