Logistics IS resources, organizational factors, and operational performance
Bibliographic record
Abstract
Purpose Drawing on the resource-based view and resource complementarity theory, the purpose of this paper is to investigate two research questions: To what extent are logistics information system (IS) resources associated with improved operational performance? And to what extent are these relationships contingent on organizational factors? Design/methodology/approach A conceptual model with a nested structure is presented to link logistics IS resources and organizational factors with operational performance. The findings are validated using a cross-sectional sample of secondary data from domestic logistics firms in China. Findings This paper extends existing operational-level measures for logistics IS resources into a three-tier tactical-level typology: inside-out resources (operation-focused IS, decision-focused IS and IS development capability); outside-in resources (relation-focused IS and market-focused IS); and spanning resources (IS integration capability and IS management capability). Though logistics IS resources, in general, are positively related to operational performance, inside-out IS resources have the most significant impact. Organizational factors, such as firm size, firm age and firm ownership, may enhance or suppress the effects of logistics IS resources on performance. Practical implications The findings are valuable to both logistics firms and buyer firms in an emerging market, as logistics IS resources may affect costs and quality of logistics service. The tactical-level typology allows logistics firms to better plan for and manage emerging IS resources in a competitive environment. Originality/value This paper extends prior work regarding the complementary effects of logistics IS resources and organizational factors on operational performance. Logistics firms should carefully manage the three types of tactical-level IS resources according to their organizational environment to achieve a sustainable competitive advantage.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".