Submitted:
10 November 2023
Posted:
13 November 2023
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Abstract
Keywords:
1. Introduction
- Speed of development: after a start-up or spin-off, a new business entity can benefit from outsourcing to implement key functions much faster and cheaper than building its own capabilities from scratch;
- Flexibility: outsourcing can provide the flexible capacity that a rapidly growing business might need to keep up with changing demand;
- Specialised skills: in specialised areas such as IT, attracting, developing and retaining skilled staff can be a real challenge: external service suppliers often offer access to these rare skills;
- Political factors: offshoring can be a sensitive topic - outsourcing to a supplier who can then use its own near-shore and off-shore capabilities may be a politically acceptable way to achieve the same goal.
2. Materials and Methods
- These organisations outsource IT activities most frequently and to the greatest extent [22];
- They are relatively stable entities and do not undergo transformations as frequently as smaller organisations;
- They use ITO earlier than other organisations, if only for economic reasons;
- They use different types and forms of outsourcing.
2.1. Description of the problem
2.2. Hypotheses
3. Results
4. Discussion
- changes in the impact of risk on the benefits of outsourcing in the post-pandemic period;
- research on other economic entities (the study presented focused on large companies and organisations in Poland).
Supplementary Materials
Author Contributions
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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| No | Branches | % |
|---|---|---|
| 1 | Industry | 45.0 |
| 2 | Trade | 14.4 |
| 3 | Services | 12.4 |
| 4 | Logistics / transport | 4.3 |
| 5 | Science / research / education | 11.0 |
| 6 | Administration / organizations / agencies | 12.9 |
| No | Latent variable | Identification | Explanation | Identification | Observable variable |
|---|---|---|---|---|---|
| 1 | endogenic | E | importance of economic risk | P16.05 | unclear relationships between costs and benefits |
| P16.07 | lack of control over salary costs | ||||
| P16.10 | hidden contract costs | ||||
| 2 | SUP | importance of supplier risk | P16.01 | qualifications of the service supplier's staff | |
| P16.02 | overdependence on supplier | ||||
| P16.03 | supplier's failure to comply with a contract | ||||
| P16.04 | supplier's inability to adapt quickly to new technologies | ||||
| 3 | CLI | importance of customer risk | P16.09 | loss of knowledge and basic skills | |
| P16.12 | possible employee resistance | ||||
| P16.15 | loss/dilution of competences | ||||
| 4 | SEC | importance of security risk | P16.06 | attacks from outside | |
| P16.08 | data corruption/loss | ||||
| P16.13 | security problems | ||||
| P16.17 | exposure to the use of sensitive and confidential data | ||||
| 5 | exogenic | EB | economic benefits | P21.07 | personnel cost savings |
| P21.09 | cost savings of using technology | ||||
| 6 | TB | technological benefits | P21.01 | increasing flexibility of the IT department | |
| P21.06 | technological conditions | ||||
| P21.08 | facilitating access to new technologies | ||||
| 7 | OB | organisational benefits | P21.04 | improving the quality of services offered | |
| P21.06 | technological conditions | ||||
| P21.08 | facilitating access to new technologies | ||||
| P21.10 | reducing the risk of technological obsolescence | ||||
| 8 | SB | strategic benefits | P21.03 | possibility to focus on strategic issues | |
| P21.05 | access to new international markets |
| Variable | Latent variable | Number of original variables composing the latent variable | Cronbach's alpha |
|---|---|---|---|
| Validity of the risk of ITO use | E (economic) | 3 | 0.887 |
| SUP (supplier related) | 4 | ||
| CLI (customer related) | 3 | ||
| SEC (security related) | 4 | ||
| Benefits of ITO | EB (economic) | 2 | 0.830 |
| TB (technological) | 4 | ||
| OB (organisational) | 4 | ||
| SB (strategic) | 2 |
| No | Latent variable | Identification | AVE |
|---|---|---|---|
| 1 | importance of economic risk | E | 0.516 |
| 2 | importance of supplier-related risk | SUP | 0.522 |
| 3 | importance of customer-related risk | CLI | 0.646 |
| 4 | importance of security risk | SEC | 0.607 |
| 5 | economic benefits | EB | 0.314 |
| 6 | technological benefits | TB | 0.360 |
| 7 | organisational benefits | OB | 0.579 |
| 8 | strategic benefits | SB | 0.417 |
| No | Measure | Value | Explanation | Result |
|---|---|---|---|---|
| 1 | Standardized Root Mean Squared Residual (SRMR) | 0.118 | A coefficient indicating the degree of misfit. The model is fit to the data when the value of this indicator is less than 0.05 [30]. | Coefficient is quite low, it does not meet the assumed condition |
| 2 | Root Mean Square Error of Approximation (RMSEA) | 0.056 | Average approximation error of the sample to the ideal population. The model is fit to the data when the index value is less than 0.05 [31] | Coefficient slightly exceeds the assumed value |
| 3 | Comparative Fit Index (CFI) | 0.520 | The comparative fit index (CFI) analyzes the model fit by examining the discrepancy between the data and the hypothesized model, while adjusting for the issues of sample size inherent in the chi-squared test of model fit, and the normed fit index. CFI values range from 0 to 1, with larger values indicating better fit [32]. | Coefficient has an average value |
| 4 | Tucker-Lewis Index (TLI) | 0.449 | Also known as the non-normed fit index, is one of the numerous incremental fit indices widely used in linear mean and covariance structure modeling, particularly in exploratory factor analysis, tools popular in prevention research [33], [34] TLI values exceeding 0.95 indicate good model fit [35]. | Coefficient does not reach the expected value |
| 5 | Bentler-Bonett Normed Fit Index (NNFI) | |||
| 6 | Bentler-Bonett Normed Fit Index (NFI) | 0.336 | An incremental measure of goodness of fit for a statistical model, which is not affected by the number of parameters/variables in the model. Goodness of fit is measured through a comparison of the model of interest to a model of completely uncorrelated variables [36]. | Coefficient has an average value |
| 7 | Bollen’s Relative Fit Index (RFI) | 0.237 | The Relative Fit Index is not guaranteed to vary from 0 to 1. However, RFI close to 1 indicates a good fit. IFI: the Incremental Fit Index (IFI) adjusts the Normed Fit Index (NFI) for sample size and degrees of freedom [37]. Over 0,9 is a good fit, but the index can exceed 1 [38]. | Coefficient has a low value |
| 8 | Bollen’s Incremental Fit Index (IFI) | 0.570 | It adjusts the Normed Fit Index for sample size and degrees of freedom. Over 0,9 is a good fit, but the index can exceed 1 [37]. | Coefficient has an average value |
| 9 | Parsimony Normed Fit Index (PNFI) | 0.292 | Parsimony-corrected fit indices are relative fit indices that are adjustments to most of the fit indices mentioned above. The adjustments are to penalize models that are less parsimonious, so that simpler theoretical processes are favored over more complex ones. The more complex the model, the lower the fit index [39]. | Correct value |
| Hypothesis | Testing result | Evaluation | |
|---|---|---|---|
| 1 | The achievement of strategic benefits from ITO is positively influenced by the importance of ITO security risks. | Accepted | Statistically significant positive impact, small impact |
| 2 | The achievement of organisational benefits from ITO use is positively influenced by the importance of the risks associated with the ITO client. | Accepted | Statistically significant positive impact, slightly higher impact |
| 3 | The achievement of technological benefits from ITO use is positively influenced by the importance of ITO supplier risk. | Accepted | Statistically significant positive impact, medium level impact, highest of assumed |
| 4 | The achievement of economic benefits from ITO is positively influenced by the importance of economic risk. | Accepted | Statistically significant positive impact, medium level impact |
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