# Critical Success Factors Evaluation for Blockchain’s Adoption and Implementing

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

- What are the CSFs of blockchain technology from a broad perspective and not for specific applications?
- What are the types of relationships among factors, and what is the significance of these relations?
- What measures can be used to quantify the success of a blockchain?

## 2. Related Work

## 3. Research Model

- Phase 1: “Search using search terms”.
- Phase 2: “Exclusion based on title and abstract”.
- Phase 3: “Exclusion based on introduction and conclusions”.
- Phase 4: “Exclusion based on the full text”.
- Phase 5: “Final selection of primary studies”.

#### 3.1. Blockchain Success Factors

#### 3.1.1. Technological Context

#### 3.1.2. Organizational Context

#### 3.1.3. Environmental Context

#### 3.2. Blockchain Success Indicators

#### 3.2.1. Overall Performance

#### 3.2.2. System Robustness

#### 3.2.3. Data Robustness

#### 3.2.4. Accessibility

#### 3.2.5. Overall Cost

## 4. The Relationships among the Factors

#### 4.1. Hierarchical DEMATEL

- Generate the average direct-relation matrix “X”;
- Normalize the direct-relation matrix “N”;
- Obtain the total direct-relation matrix “T”;
- Compute prominence and relations between factors.

- Step H1. Hierarchical decomposition

- Step H2. Direct influence analysis

- Step H3. Construct the super initial direct-relation matrix

_{qq′}]

_{Q×Q}included in system F and X

_{q}= [${x}_{nn\prime}^{q}$]

_{Nq×Nq}included in the subsystem Fq, q = 1,⋯, Q. A pair of subsystems’ direct interaction in the one-level subsystem structure can be described by Equations (1) and (2):

_{q}→F

_{q′}indicates the direct influences among the subsystem’s constituent factors ${x}_{ij}^{qq\prime}$ = ${x}_{nn\prime}^{q}$; (∀i, j) is obtained within the IDR matrix using Equation (3):

_{q}→F

_{q′}signifies the direct effects among the factors included in two diverse subsystems F

_{q}and F

_{q′}, whose degrees ${x}_{ij}^{qq\prime}$ are not obtained directly and need to be calculated.

_{q}→F

_{q′}is equivalent to the total of a factor’s direct influence degrees ${f}_{i}^{q}$ →${f}_{j}^{q\prime}$, i.e., x

_{qq′}= δ

_{qq′}Σ

_{i}Σ

_{j}${x}_{ij}^{qq\prime}$, where δ

_{qq′}is a coefficient of conversion between the two neighboring levels produced by the relative 0–4 scales. To unite the units of the scales at the lower level (factor) with those at the upper level (subsystem), the conversion coefficient δqq′ is added. As already stated, when q = q′, xqq′ is the degree of direct influence on subsystems and ${x}_{ij}^{qq\prime}$ (I, j = 1,⋯, N

_{q}); experts have provided information on factors. Consequently, Equation (4) can be used to determine the conversion coefficient.

_{q}can be changed using Equation (5):

_{q}→F

_{q′}can be constructed as ${\overline{x}}_{q{q}^{\prime}}={\left[{\overline{x}}_{ij}^{qq\prime}\right]}_{{N}_{q}\times {N}_{q\prime}}$ [23]. As a result, Equation (6) can be used to characterize the direct influence degree among factors on the same subsystem and various subsystems.

_{q}= [${t}_{ii\prime}^{q}$]

_{Nq×Nq}in the subsystem F

_{q}. The super IDR matrix can be created using the IDR matrices for all subsystem pairs using Equation (7):

#### 4.2. Quantifying the Relationship among the Factors

_{1⊃1}), organizational (F

_{1⊃2}), and environmental (F

_{1⊃3}). The factor set F

_{1}= {f

_{1},⋯, f

_{14}} and the two-level structure of blockchain implementation is shown in Table 5. This sub-section presents the results after applying the hierarchical DEMATEL technique.

_{1}and X

_{1⊃q2}, q

_{2}= 1, ⋯, 3) which show the direct relationships between and within the subsystems F

_{1⊃q2}. In addition, the values on the main diagonal of the IDR matrices implicated in the level-1 subsystems, as shown in Equation (8), may be greater than zero, indicating the factors involved in those subsystems may effect each other. However, the values on the primary diagonal of the IDR matrices implicated in the level-2 subsystems, as shown in Equations (9)–(11), are equal to zero, indicating that no direct influence exists between the factors and themselves.

_{1⊃2}described by the IDR matrix X

_{1⊃2}can be calculated as shown in Equation (12).

_{2}= 3 and q′

_{2}= 2, the direct influence degrees on F

_{1⊃3}→F

_{1⊃2}are the value 1 in the IDR matrix X

_{1}; it means the sum of the direct-effect degrees that the elements included in subsystem F

_{1⊃3}influence those in F

_{1⊃2}is 1. Table 6 displays the outcomes of determining the prominences of the two subsystems’ total IDR matrices for the concerned factors using steps 2–3 in Section 4.1. Thus, the IDR matrix on F

_{1⊃3}→F

_{1⊃2}can be determined as:

_{2}, q′

_{2}= 1,2, 3, the determined IDR matrices ${\overline{x}}_{1\supset {q}_{2}q{\prime}_{2}}$ are integrated to create the super IDR matrix X1.

## 5. Results and Discussions

_{1}, the DEMATEL is applied to generate the total relation matrix. Then, the prominence and ranking of every factor are then determined, as shown in Table 7. As a result, when the value of (R − D) is positive, the success factor is a net causer. On the other hand, when the value of (R − D) is negative, the success factor behaves as a net receiver [74]. The experts ranked the environmental context as the most critical one for achieving successful blockchain adoption. Therefore, law and policies and competitive pressure are the top two factors needed for blockchain adoption. Among the technological context, only the scalability of blockchain is considered the top significant factor for blockchain adoption. Regarding the organizational context, adequate resources, top management support, and financial constraints are highly ranked.

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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Author | Research Area | Tools Used | Critical Factors Identified | Successful Implementation |
---|---|---|---|---|

Post et al. [12] | Blockchain | Grounded theory-based data collection and analysis | Strategic (sector pressure, organizational size) Tactical (knowledge deficit, implementation method) Operational (technical shortcomings, process maturity) | x |

Zhou et al. [13] | Maritime industry | AHP, a fishbone diagram and PESTEL analysis | Lack of experience, lack of blockchain knowledge, and scalability | x |

Juliet et al. [14] | Freight-logistics industry | ANP | Technological (infrastructural facility, complexity, compatibility) Organizational (training facilities, top management support) External environmental context (law and policy, competitive pressure) | x |

Prasad et al. [15] | Cloud services | Total interpretive structural modeling (TISM) | Regulatory clarity, and law of experiences | x |

Shardeo et al. [3] | Supply chain | AHP | x | System strength (transparency, disintermediation, immutability) Overall efficiency (effectiveness and efficiency, automation) Reliability and ecoreconciliation (reliability, immutability, decentralization) overall cost |

Kouhizadeh et al. [18] | Supply chains | DEMATEL | Technological (infrastructural facility, immaturity) Organizational (financial constraints, top management support) Environmental (lack of governmental policies) | x |

Inclusion Criteria | Exclusion Criteria |
---|---|

Published from 2018 to 2022 | Published not in the English language |

In the field of block chain | Not relevant to challenges or success factors |

Presented empirical data | Review papers or non-technical papers |

Peer-reviewed | Grey literature (white papers, editorial comments, book reviews) |

Categories | Factors |
---|---|

Technology | Scalability [13,30,31] |

Infrastructural facility [14,18,22,32,33,34,35,36] | |

Complexity [14,32,33] | |

Compatibility [14,32,33,34,35] | |

Immaturity of Technology [18,22,37] | |

Distributed design [22] | |

Organization | Lack of experience and knowledge [13,15,32,38,39] |

Training facilities [13,27,30,31,32,36,37,38,39,40,41] | |

Top management support [14,18,30,33,34,35,37,39,40,41,42,43,44,45,46] | |

Organizational culture [13,14,18,30,33,34,37,39,40,43,44,45,46] | |

Financial Constraints [18,22,33,37,47,48,49] | |

Adequate resource [37] | |

Environment | Laws and Policy [14,19,22,30,31,32,33,35,36,39,45,46,47,50,51] |

Competitive pressure [14,30,35,36,39,45,46,50] |

Measures | Factors |
---|---|

Overall performance | Efficiency [19,38,60] |

Effectiveness [19,38,60] | |

Speed [3,22,31,61,62] | |

Quality [3,16] | |

System robustness | Transparency [3,4,19,31,38,60,61,62] |

Security [3,4,19,31,38,60,61,62] | |

Disintermediation [3,19] | |

Trust [4,19,60] | |

Data | Immutability [3,19,31,60,63] |

Reliability [3,4,60] | |

Decentralization [3,19] | |

Data accuracy [31] | |

Accessibility | Traceability [3,4,19,37,38,60,63,64] |

Integrity [3] | |

Overall cost | Cost reduction [3,4,19,31,32,60] |

Save energy [3] |

Dimension | Factor | Criteria |
---|---|---|

Technological (F_{1⊃1}) | f_{1} f _{2} f _{3} f _{4} f _{5} f _{6} | Scalability Infrastructural facility Complexity Compatibility Immaturity of technology Distributed design |

Organizational (F_{1⊃2}) | f_{7} f _{8} f _{9} f _{10} f _{11} f _{12} | Lack of experience and knowledge Training facilities Top management support Organizational culture Financial constraints Adequate resource |

Environmental (F_{1⊃3}) | f_{13} f _{14} | Laws and policy Competitive pressure |

Subsystem | Factor | ${\mathit{x}}_{1}^{1\supset {\mathit{q}}_{2}}$ | ${\mathit{x}}_{2}^{1\supset {\mathit{q}}_{2}}$ | ${\mathit{x}}_{3}^{1\supset {\mathit{q}}_{2}}$ | ${\mathit{x}}_{4}^{1\supset {\mathit{q}}_{2}}$ | ${\mathit{x}}_{5}^{1\supset {\mathit{q}}_{2}}$ | ${\mathit{x}}_{6}^{1\supset {\mathit{q}}_{2}}$ | r_{i} | d_{i} | ${\mathit{z}}_{\mathit{i}}^{1\supset {\mathit{q}}_{2}}$ |
---|---|---|---|---|---|---|---|---|---|---|

F_{1⊃2} | ${x}_{1}^{1\supset 2}$ | 0.215 | 0.299 | 0.270 | 0.366 | 0.326 | 0.339 | 1.817 | 2.300 | 4.118 |

${x}_{2}^{1\supset 2}$ | 0.382 | 0.276 | 0.251 | 0.395 | 0.355 | 0.411 | 2.072 | 2.612 | 4.684 | |

${x}_{3}^{1\supset 2}$ | 0.521 | 0.615 | 0.306 | 0.580 | 0.584 | 0.611 | 3.218 | 1.828 | 5.047 | |

${x}_{4}^{1\supset 2}$ | 0.314 | 0.344 | 0.269 | 0.231 | 0.327 | 0.342 | 1.829 | 2.428 | 4.257 | |

${x}_{5}^{1\supset 2}$ | 0.448 | 0.531 | 0.343 | 0.422 | 0.331 | 0.528 | 2.605 | 2.441 | 5.047 | |

${x}_{6}^{1\supset 2}$ | 0.418 | 0.545 | 0.388 | 0.432 | 0.516 | 0.366 | 2.666 | 2.598 | 5.264 | |

F_{1⊃3} | ${x}_{1}^{1\supset 3}$ | 2 | 3 | — | — | — | — | 5 | 4 | 9 |

${x}_{2}^{1\supset 3}$ | 2 | 2 | — | — | — | — | 4 | 5 | 9 |

Factor | ${\overline{\mathit{r}}}_{\mathit{i}}-{\overline{\mathit{d}}}_{\mathit{i}}$ | ${\overline{\mathit{r}}}_{\mathit{i}}+{\overline{\mathit{d}}}_{\mathit{i}}$ | Ranking | ||
---|---|---|---|---|---|

Technological | f_{1} | Scalability | −0.23 | 5.73 | 3 |

f_{2} | Infrastructural facility | −0.22 | 4.78 | 8 | |

f_{3} | Complexity | −0.57 | 4.64 | 12 | |

f_{4} | Compatibility | −0.36 | 4.76 | 11 | |

f_{5} | Immaturity of technology | −0.37 | 4.76 | 10 | |

f_{6} | Distributed design | −0.51 | 4.77 | 9 | |

Organizational | f_{7} | Lack of experience and knowledge | 0.10 | 4.23 | 14 |

f_{8} | Training facilities | 0.11 | 4.84 | 7 | |

f_{9} | Top management support | 1.08 | 5.25 | 5 | |

f_{10} | Organizational culture | 0.05 | 4.38 | 13 | |

f_{11} | Financial constraints | 0.48 | 5.22 | 6 | |

f_{12} | Adequate resource | 0.44 | 5.43 | 4 | |

Enviomental | f_{13} | Laws and policy | 0.45 | 7.17 | 1 |

f_{14} | Competitive pressure | −0.43 | 7.17 | 2 |

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## Share and Cite

**MDPI and ACS Style**

Grida, M.O.; Abd Elrahman, S.; Eldrandaly, K.A.
Critical Success Factors Evaluation for Blockchain’s Adoption and Implementing. *Systems* **2023**, *11*, 2.
https://doi.org/10.3390/systems11010002

**AMA Style**

Grida MO, Abd Elrahman S, Eldrandaly KA.
Critical Success Factors Evaluation for Blockchain’s Adoption and Implementing. *Systems*. 2023; 11(1):2.
https://doi.org/10.3390/systems11010002

**Chicago/Turabian Style**

Grida, Mohamed O., Samah Abd Elrahman, and Khalid A. Eldrandaly.
2023. "Critical Success Factors Evaluation for Blockchain’s Adoption and Implementing" *Systems* 11, no. 1: 2.
https://doi.org/10.3390/systems11010002