Applicability of Both/and Thinking in International Sustainable Business Studies
Bibliographic record
Abstract
The purpose of this article is to provide a methodological demonstration making use of the both/and thinking (BAT) framework to perform analysis of intertemporal tensions. The BAT framework as an analytical tool is able to holistically examine complex multi-level business problems that involve tensions, contradictions and paradox that could be useful to others. Engaging the BAT framework in international sustainable business studies can be a challenging choice as it requires holistic understanding of the affects in the separation of contradictory elements across time and distance to shape research inquiry and direction. The approach considers that while paradoxes deal with contradictions, as a methodological process it enables a strategy for juxtaposing apparent opposites using an integrative lens embedded in BAT. As such, the use of BAT is discussed using a constructionist approach to gain insight and understanding on surfacing intertemporal tensions exemplified by the socio-business case study that is situated in the chocolate industry. The article draws on BAT primary and sub-themes and discusses implications and applications as a technique to frame the grappling of tensions. The findings are guided by the existing literature and the analysis from the empirical case study providing contributions in practice to further support the use of the BAT framework. Paradoxes examined in this article are based on affects for the themes of organizing, belonging, performing and learning. This article provides understanding of the findings to gain insight from an empirical and theoretical perspective to illustrate the practical implications of the methodological approach. As such, the principles of paradox theory are placed in the context of the BAT framework which are exemplified by making use of the empirical case study data to surface the potential applicability of the approach for future research. This article aims to contribute to the business and management methodological literature by demonstrating the use of the BAT approach and contributes with specificity in relation to the paradox taxonomy and the use of the BAT framework. Despite certain limitations, the BAT framework can be an excellent choice for qualitative sustainable business research that deals with contradictory demands.
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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".