DOI: 10.5061/dryad.fj6q5745d
title: Statistical approaches in accounting for optimal choice and business location decisions

description: 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.
description: Data was collected through the use of structured questionnaire,
 collated and aggregated using the formula: <strong>value = (score/n)
 * 10</strong> Where,        
  <strong>n</strong> = number of business or sales outlets
 under the control of the respondent        
  <strong>score</strong> = 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).        
  <strong>value</strong> = figure entered in the Excel sheet
 cell
description: # Statistical approaches in accounting for optimal choice and business
 location decisions
 [https://doi.org/10.5061/dryad.fj6q5745d](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