Assessing the Relationship between Innovation and Survival in Organizations: An Empirical Review, Research Synthesis, and Analytical Case Study
Bibliographic record
Abstract
IntroductionAlthough considerable effort has been made to promote innovation and identify how to manage it, much less work has gone into considering the effects of innovations. This study considers the effects of innovations on their organizations and consolidates what is known now about these effects, then considers new methodological implications of this research synthesis, including the aptness of the case study method, movement among macro-, meso-, and micro-level analyses (i.e., inter-organizational, organizational/group, and individual levels of analysis), and prospects for resource-generation, structural-change, and other established theoretical streams. First, it examines the effects of innovations on their organizations by identifying a research framework, examines the relevant literature for normal and changed organizational populations that might apply to each hypothesis, develops four possible hypotheses about the effect of innovations on the fate of their organizations, and explores how this might apply to innovating organizations. Second, the study examines a broader range of research questions about innovation that could be examined in the future. This empirical review concludes with a case study causally linking project termination and organizational crisis, and innovation and survival. It is an historic study of the U.S. Bureau of Land Management (BLM) Automated Land and Mineral Record System (ALMRS), from its genesis to its demise, and the birth of its successor National Integrated Land System (NILS).The Effects of Innovations on their OrganizationsPerhaps those who fear the effects of innovation on organizations are correct: it may threaten the organization-or it may help the organization survive. Without integrative research on the subject, no one will ever know with any certainty. Researchers studying these issues face numerous challenges-isolating the effects of innovations from those of other factors, identifying ways to identify the effects of innovations on their organizations, and ascertaining how to deal with the different levels of innovations, organizations, organizational communities and populations. These require different levels of analysis, from individual to group to organizational and inter-organizational levels, as to causal paths and impacts, with qualtitative and quantitative research.While organizations can be created or reorganized to facilitate changes in policies, processes and programs, at the same time, Organizational usually accompanies policy change (Lewis, 2002: 102). Both innovations and their organizations must thus be considered. The following section of the study focuses on a research framework for studying and the effects of innovations on their organizations and leaves the separation of innovation from other factors influencing the fate of organizations for another time.Approaches to Studying the Impact of Innovation: A FrameworkIn order to study the impacts of innovations on the fate of innovating organizations, researchers must decide which issues to study and on an approach. To take these decisions, they must make judgments about what might be important to innovating organizati ons' fates.Astley and Van de Ven (1983) developed a framework that considered micro (organization)/macro (population) levels of organizational analysis and deterministic/voluntaristic considerations of human nature to create a four-type framework for analysis of organization theories. Glor (2002) examined the innovations of a population, the Government of Saskatchewan, 1971-82 in deterministic and voluntaristic terms. Van de Ven and Poole (1995) found that the variety of concepts used to study how organizations changed have led to the compartmentalization of perspectives. They developed four basic (primitive) theories to explain processes of in organizations: life cycle, teleology, dialectics and evolution and recommended considering how they interact. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".