Influences on Administrative Costs in Convenience Store Chains: A Cross-Sectional Activity-Based Study
Bibliographic record
Abstract
ABSTRACTConvenience chains have administrative functions - largely accounting, human resource, and compliance activities - to support the business operations. Chains across the United States and Canada, while rich in operational industry benchmark data, have no data to help evaluate the appropriateness of their administrative costs.Using a mixed-methods approach built on the theoretical foundation of activity-based budgeting, data was gathered and analyzed attempting to link chain activities to administrative processes. The data gathered covered chains owning from five to seventy stores.The results show that economies of scale and automation of paperwork handling, particularly as it pertains to inside-the-store activity, yield per-store savings. Increases in chain size have more potential to increase administrative costs than increases in business complexity. Specific activities are linked to per-store administrative providing practitioners with inferential guidance as to where administrative savings can be found.INTRODUCTIONConvenience chains engage in many activities from selling lottery tickets to fresh food to gasoline. Which of these activities is likely to most increase, or decrease, administrative costs?The practical research goal is to establish a statistically sound links between chain activities and administrative costs. Academically, this comprises the first industry-specific cross-sectional activity based-budgeting study.The theoretical model for this research is activity-based budgeting (ABB), a budgeting methodology derived from activity-based costing (ABC). Unlike traditional budgeting methods, ABB focuses not on spending, but on activity; what the firm does as opposed to what it spends (McClenahen, 1995). With ABB the firm's activities form pools associated with drivers to estimate future costs. ABB's historical roots can be traced to ABC and budgeting (Abemethy & Vagnoni, 2004; Beatty, 2007; Hopwood, 1974).To accomplish its objectives this research pursues four specific outcomes: (a) A clear repeatable measurement of administrative costs; (b) the establishment of a data model materially free of intervening variables to establish valid correlations between activities and administrative costs; (c) a cross-sectional survey to collect industry data to establish an initial set of norms for subsequent comparison; and (d) results providing traceability between activities and administrative costs.RESEARCH QUESTIONWhich activity measures of a convenience chain drive administrative costs?GENERAL HYPOTHESISThe central number of interest, and dependent variable for this analysis, is per-store administrative Practical experience and anecdotal observation indicates that per-store administrative is measurably influenced by four attributes of a convenience chain - chain size, business complexity, organizational capabilities, and organization characteristics.The specific definition of administrative costs is detailed in the methodology section, but generally it could be described as headquarters cost as opposed to store cost. Per-store administrative is defined as total administrative costs divided by total company-owned, company-operated stores.Specific HypothesesFor each of the four major areas - chain size, business complexity, organizational capability, and organizational characteristics - proxies have been identified, beginning with chain size proxies.Chain SizeThere are three measures of chain size expected to influence per-store administrative costs:Smaller companies, on the whole, have been shown to bear a proportionally larger burden for administrative costs than larger companies (Nair & Rittenberg, 1983); therefore, economies of scale should emerge as the quantity of COCO stores increases.Dealer locations are those operated by independent businesses under a licensing agreement containing image restrictions and requirements to purchase fuel from the convenience chain. …
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".