Exploring the Effects of Liminality on Corporate Social Responsibility in Interfirm Outsourcing Relationships
Bibliographic record
Abstract
This chapter seeks to contribute to the corporate social responsibility (CSR) discourse of “doing well by doing good” in the domain of Global Information Technology Outsourcing (GITO). Matten and Moon (2008) define CSR as a “clearly articulated and communicated set of policies and practices of corporations that reflect business responsibility for some of the wider societal good” (p. 405). Authors including Bishop and Green (2008), Emerson (2003), and Porter and Kramer (2006, 2011) have all argued for a “doing well by doing good” approach to corporate social responsibility that utilizes pro-market strategies to increase returns on philanthropic investment. They posit that corporations that embrace social concerns create a “win-win” outcome for both parties (Falck and Heblich 2007). However, Ahmad and Ramayah (2013) highlight the controversy that exists, questioning whether ventures that devote resources and effort in trying to improve society will suffer in terms of performance, or whether enterprises that “do good” will also “do well,” and thus be successful both financially and socially. This issue remains inconclusive, as prior studies have presented mixed results (Roper and Parker 2013), highlighting the need for further empirical research. Furthermore, prior studies have largely been situated within a firm hierarchy or strategic alliance, and there is a paucity of literature exploring how the “doing well by doing good” approach to CSR might prevail in market-based interfirm outsourcing relationships. GITO presents an interesting context in which to study this phenomena, as it involves the subcontracting of IT services by transacting partners (client to a vendor) with some or all of the tasks undertaken in a different country (Sahay, Nicholson, and Krishna 2003).
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".