Table 1.
Own research questionnaire
| Question number | Question | Answer variants |
|---|---|---|
| 1 | How many people does your company currently employ? | Number of employees |
| • Agriculture, forestry, hunting and fishing | ||
| • Industry, mining and energy | ||
| • Construction | ||
| • Wholesale and retail trade; vehicle repair | ||
| • Transport and warehousing | ||
| • Accommodation and catering | ||
| 2 | Branch | • Information and communication (IT, media, telecommunications) |
| • Financial and insurance activities | ||
| • Activities related to real estate market services | ||
| • Professional, scientific and technical activities (law, consulting, engineering) | ||
| • Administrative and support services activities (HR, leasing, service) | ||
| • Public Administration and Education | ||
| • Healthcare and social assistance | ||
| Culture, entertainment, recreation and other services | ||
| 3 | How long has the company been operating on the market? | In years |
| 4 | How often is business decision-making based on data | Scale 1–5 (1 – never, 5 – regularly) |
| 5 | What areas of the company's operations are analyzed based on measurable data? | |
| 5.1 | HR | |
| 5.2 | Pricing | |
| 5.3 | Financial and sales forecasts | Likert scale 1–5 (1 – never, 5 – regularly) |
| 5.4 | Cost analysis | |
| 5.5 | Staff efficiency | |
| 5.6 | Investment profitability analysis | |
| 6 | What areas of your business do you plan to analyze based on measurable data over the next 12 months? | |
| 6.1 | HR | |
| 6.2 | Pricing | |
| 6.3 | Financial and sales forecasts | Likert scale 1–5 (1 – never, 5 – regularly) |
| 6.4 | Cost analysis | |
| 6.5 | Staff efficiency | |
| 6.6 | Investment profitability analysis | |
| 7 | What data sources are used in your enterprise? | |
| 7.1 | Data from accounting systems | |
| 7.2 | Data from CRM/ERP systems | Likert scale 1–5 (1 – never, 5 – regularly) |
| 7.3 | Social media | |
| 7.4 | Internal research | |
| 7.5 | External market data | |
| 8 | What do you think are the barriers to data analysis in the enterprise: | |
| 8.1 | Lack of analytical skills among employees | |
| 8.2 | Lack of time | |
| 8.3 | Low level of digitization within the company | Likert scale 1–5 (1 – very strong barrier, 5 – very weak barrier) |
| 8.4 | Employee resistance | |
| 8.5 | Lack of appropriate IT tools and systems | |
| 8.6 | Difficulty integrating data from various sources | |
| 8.7 | Concerns about data security and confidentiality | |
| 9 | How do you assess data quality in your organization? | Scale 1–5 (1 – very low quality, 5 – very good quality) |
| 10 | Does the company use artificial intelligence (AI) or machine learning (ML) algorithms? | Scale 1–5 (1 – never, 5 – regularly) |
| 11 | Does management have the skills to independently interpret advanced reports? | Yes/No |
| 12 | Does the company have a dedicated department for data collection/processing? | Yes/No |
| 13 | Does the company plan to increase time investment in data analytics over the next year? | Scale 1–5 (1 – definitely no, 5 – definitely yes) |
| 14 | Does the company plan to increase engagement in data analytics over the next year? | Scale 1–5 (1 – definitely no, 5 – definitely yes) |
Table 2.
Tau-Kendall coefficients – company size/time on the market and frequency of using data in making managerial decisions
| Factor | Tau-Kendall coefficient |
|---|---|
| Company size | 0.529 |
| Time on the market | 0.365 |
Table 3.
Tau-Kendall coefficients – company size/time on the market and using various data sources
| Factor | Data from accounting systems | Data from CRM/ERP systems | Social media | Own research | External market data |
|---|---|---|---|---|---|
| Company size | 0.410 | 0.655 | 0.261 | 0.714 | 0.672 |
| Time on the market | 0.404 | 0.523 | 0.232 | 0.570 | 0.515 |
Table 4.
Model parameters
| Regression Statistics | ||||||
| Multiple R | 0.59 | |||||
| R Square | 0.35 | |||||
| Fitted R Square | 0.32 | |||||
| Standard Error | 0.46 | |||||
| Observations | 99 | |||||
| Variance Analysis | df | SS | MS | F | F significance | |
| Regression | 5 | 10.5 | 2.1 | 10.1 | 0.00000010 | |
| Residual | 93 | 19.34 | 0.21 | |||
| Total | 98 | 29.84 | ||||
| Coefficients | Standard error | t Stat | p-value | Lower 95% | Upper 95% | |
| Intersection | 2.94 | 0.21 | 14.2 | - | 2.53 | 3.36 |
| X1 | 0.001 | 0.0006 | 1.68 | 0.09 | −0.0001 | 0.002 |
| X2 | 0.02 | 0.01 | 2.57 | 0.01 | 0.01 | 0.04 |
| X3 | −0.04 | 0.05 | −0.74 | 0.46 | −0.14 | 0.06 |
| X4 | 0.03 | 0.07 | 0.49 | 0.63 | −0.1 | 0.17 |
| X5 | 0.43 | 0.15 | 2.89 | 0.004 | 0.14 | 0.73 |
Table 5.
Model parameters
| Regression Statistics | ||||||
| Multiple R | 0.36 | |||||
| R Square | 0.13 | |||||
| Fitted R Square | 0.05 | |||||
| Standard Error | 0.54 | |||||
| Observations | 99 | |||||
| Variance Analysis | df | SS | MS | F | F significance | |
| Regression | 8 | 0.48 | 1.67 | 0.12 | 0.12 | |
| Residual | 90 | 0.29 | ||||
| Total | 98 | |||||
| Coefficients | Standard error | t Stat | p-value | Lower 95% | Upper 95% | |
| Intersection | 3.67 | 0.88 | 4.16 | 0.00007 | 1.92 | 5.43 |
| X1 | 0.0002 | 0.06 | 0.004 | 1 | −0.12 | 0.12 |
| X2 | −0.07 | 0.06 | −1.3 | 0.2 | −0.18 | 0.04 |
| X3 | −0.1 | 0.12 | −0.89 | 0.38 | −0.33 | 0.13 |
| X4 | −0.1 | 0.14 | −0.71 | 0.48 | −0.38 | 0.18 |
| X5 | 0.12 | 0.13 | 0.97 | 0.34 | −0.13 | 0.38 |
| X6 | 0.02 | 0.12 | 0.13 | 0.9 | −0.23 | 0.26 |
| X7 | −0.1 | 0.32 | −0.33 | 0.74 | −0.74 | 0.53 |
| X8 | 0.146 | 0.05 | 3.18 | 0.002 | −0.06 | 0.24 |
Chart 1.
Distribution of answers to questions no. 4 and no. 14
Source: Author's own study.