Statistical approaches in accounting for optimal choice and business location decisions
Data files
Dec 08, 2025 version files 71.79 KB
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README.md
3.01 KB
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Venture10.xlsx
68.78 KB
Abstract
Background: Choosing an optimal business location is a critical decision that can significantly impact an organization's success. Many businesses fail because their initiators were not properly guided with informed counsel prior to the commencement.
Methods: This study employed stratified random sampling to collect data from a sample of 1200 business units across the southern and middle belt regions of Nigeria. Using statistical tools such as factor analysis, empirical literature review, and ordinal logistic regression, the study explores accounting perspectives from type and locational factors necessary in making impactful business decisions. These include analyzing the turnover potential of various business types and locations.
Results: The study found that business success and sustainability are sensitive to factors such as culture, government regulation, religion, and population density. The findings also indicate that the choice of business type—such as consumer goods, industrial goods, foodstuff, supermarkets—and their locations in rural or urban areas all have significant implications for business survival.
Conclusion: By integrating accounting and statistical perspectives, businesses can make more informed and strategic location decisions. These decisions can leverage the analytical advantages of understanding business types and locations, ultimately improving operational efficiency and profitability.
https://doi.org/10.5061/dryad.fj6q5745d
This README file was generated on 2025-03-08 by Enyi Enyi
Date of Data Collection
2023-2024
Contributors
- Enyi, Patrick Enyi: Department of Accounting, Babcock University, Ilishan-Remo, Nigeria.
- Nwaobia, Apollos Nwabuisi: Department of Accounting, Babcock University, Ilishan-Remo, Nigeria.
- Olurin, Oluwatoyosi Tolulope: Department of Accounting, Babcock University, Ilishan-Remo, Nigeria.
- Onu, Gift Nkesi: Department of Accounting, Babcock University, Ilishan-Remo, Nigeria.udi Arabia.
Overview
This supplementary dataset, integral to our research paper, contains data crucial for understanding the key findings of our study. It includes aggregated survey responses from 1,200 respondents in 24 states of southern and middle belt regions of Nigeria. The dataset presents diverse business profiles and is ideal for comparative studies, business choice, and locational analysis. The data were converted from their original Likert-like scale format using the formula value = (score/n) * 10, promoting transparency and allowing for varied data processing and analysis methods, thus facilitating diverse scientific interpretations and collaborative research.
Description of the data and file structure
The dataset serves to substantiate the conclusions drawn in our study and are valuable for further scientific exploration and verification.
File: Venture-Questionnaire.docx (Zenodo)
This file contains the detailed questionnaire used to collect the data.
File: Venture10.xlsx
Description: This file, along with the data presented in the main paper, provides comprehensive support for our research findings. It includes results from Ordinal Logistic Regression advanced analytical techniques.
Variables
- consgood (Predictor variable for consumer goods)
- indgoods (Predictor variable for industrial goods)
- supmkts (Predictor variable for supermarkets and chain stores)
- Turnover (Outcome variable with 5 proxies - Below2m, B2T5m, B5T50m, B50T100m, Above100m)
- urban (Predictor variable for urban business location or sales outlet)
- rural (Predictor variable for rural business location or sales outlet)
- foodstuff (Predictor variable for foodstuffs and groceries)
Note: Each row of the Excel spreadsheet (except the headers) represents a respondent.
Code/software
Microsoft Excel version 2007 or later
Recommended Software for Data Analysis:
Ordinal Logistic Regression Data Analysis:
- Primary Software: ValuStats (VSP 2.0) from FSDC in collaboration with the Department of Accounting, Babcock University.
- Alternative Software: Python 3.12, EViews 18.0, SPSS 28.0, Stata and R.
Access information
Other publicly accessible locations of the data:
- N/A
Data was derived from the following sources:
- N/A
Data was collected through the use of structured questionnaire, collated and aggregated using the formula: value = (score/n) * 10
Where,
n = number of business or sales outlets under the control of the respondent
score = respondent's score of the occurrence of the turnover range under each of the predictors (consgood, indgoods, foodstuff, supmkts, rural, and urban). The scores ranged between 1 (least likely) and 10 (most likely).
value = figure entered in the Excel sheet cell