Reaping IT Externality Benefits Across Business Units in Multibusiness Firms
Bibliographic record
Abstract
The indirect productivity gains related to information technology (IT), known as IT externalities, in inter-firm contexts have been extensively studied. However, the impact of IT investments within a business unit (BU) of a multibusiness firm on the productivity of other BUs remains unclear. Additionally, the conditions that facilitate such intra-firm externalities are not well understood. Research on resource externalities within multibusiness firms typically focuses on capacity-sharing benefits, where unused capacity in one unit can be utilized by another. IT resources, however, often lack capacity-sharing potential due to their full utilization or contractual limitations. Despite this, IT resources can generate non-rivalrous intangibles, such as internally developed applications, expertise, and consulting know-how, which can be shared within the firm to create externalities. This study investigates whether IT centralization (ITC), as a vertical coordination mechanism, is effective in harnessing IT externality potential arising from IT portfolio similarities (ITPSs), a form of horizontal coordination, across BUs. Utilizing data from 8,374 unique units within 866 firms from 2005 to 2020, we find that BUs must meet two conditions—higher ITPS and higher levels of ITC—to realize greater intra-firm IT externality benefits. Furthermore, these benefits accrue from IT investments made by units with a sufficient number of IT employees. Interestingly, BUs with limited access to IT employees gain more from pooled IT investments. Our findings suggest that concurrent vertical and horizontal coordination, along with access to human talent for creating knowledge, code, and expertise from digital resources, are crucial for maximizing digital resource externalities.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".