AGORA International Journal of Economical Sciences, http://univagora.ro/jour/index.php/aijes ISSN 2067-3310, E-ISSN 2067-7669 Vol. 18, No. 2 (2024), pp. 375-386 375 VALUATION MODEL OF CROWDFUNDING CAMPAIGNS APPLYING SIMPLE ADDITIVE WEIGHTING METHOD S. VENSLAVIENĖ, J. KARTAŠOVA Santautė Venslavienė¹, Jekaterina Kartašova² ¹ ² Vilnius University Business School, Lithuania ¹ https://orcid.org/0000-0002-4432-6753, E-mail: santaute.venslaviene@vm.vu.lt ² https://orcid.org/0000-0003-3774-1817, E-mail: jekaterina.kartasova@vm.vu.lt Abstract: Every investor has some difficulties when investing into crowdfunding campaigns, as it is not clear how to evaluate specific crowdfunding campaign or what success factors to choose. The aim of this study is to propose the crowdfunding campaign assessment model, test it empirically and illustrate how to select the most appropriate crowdfunding campaign for individual investor to invest. Multi-criteria methods, used in the evaluation process, enable to get objective answers about the effectiveness of the optimal crowdfunding campaign comprehensively by presenting some generalized indicators and considering both quantitative and qualitative data. The obtained empirical results comparing two crowdfunding campaigns show that the proposed method could be used for evaluating complex processes of the optimal crowdfunding investments, and could be adapted for various situations. Keywords: crowdfunding, crowdfunding campaign, valuation model, multi-criteria decision method, simple additive weighting INTRODUCTION The crowdfunding industry increased significantly after the 2008 global financial crisis, as the typical financial system, especially the banking sector, was no longer trusted. Since then, crowdfunding has thrived globally (Jalal et al., 2024). Crowdfunding, being one of the key applications of Fintech that may disrupt traditional financial intermediation, is an emerging financing alternative form that connects those who can invest money directly with those who need financing for a specific project (Pandey et al., 2024; Wan et al., 2023). It is an internet- based way for companies, organizations or individuals to raise money through either donations or investments from multiple individuals (Hussain et al., 2023). The basic principle of crowdfunding is therefore to pool money from a group of individuals instead of professional parties (Mora-Cruz & Palos-Sanchez, 2023). The definitions of crowdfunding might be different, but they summarize the following key components: 1) raise funds in minor amounts; 2) many-to-many platform and 3) use of digital technology (Hussain et al., 2023; Mora-Cruz & Palos-Sanchez, 2023). These days many crowdfunding campaigns are emerging. Due to the high variety of crowdfunding campaigns, it is very difficult to select the right one. In order to select the most wanted campaign, the crowdfunding campaigns must be evaluated whether it is worth to invest or not. It is very difficult to assess the crowdfunding campaign, as most campaigns are from https://orcid.org/0000-0002-4432-6753 mailto:santaute.venslaviene@vm.vu.lt https://orcid.org/0000-0003-3774-1817 mailto:jekaterina.kartasova@vm.vu.lt VALUATION MODEL OF CROWDFUNDING CAMPAIGNS APPLYING SIMPLE ADDITIVE WEIGHTING METHOD 376 new companies which still do not have much accounting information or any tangible wealth. Also, there are not many specific success factors of crowdfunding campaigns to look. The proposed model identifies the success factors and evaluates new campaigns from investor’s perspective and reflects both their financial performance and overall attraction. This model is based on a multi-criteria decision weighting methodology and to be more precise – Simple Additive Weighting (SAW) method. The main benefits of this method are 1) ability to combine; 2) find relations; 3) evaluate both quantitative and qualitative criteria. This model follows main concept of all multi-criteria evaluation methods – it integrates the criteria values and weights into a single magnitude. The general Simple Additive Weighting model framework is adopted to fit specifically crowdfunding campaigns. This model can be used by any individual investor having chosen the proposed criteria to evaluate the crowdfunding campaigns and make investment decisions for the most exciting campaigns. For application purposes, two different campaigns were discussed in the evaluation model. The main goal of the paper is to propose the crowdfunding campaign assessment model, test it empirically and illustrate how to select the most appropriate crowdfunding campaign for individual investor to invest. Three tasks were developed: 1) To identify the main success factors that influence the value of crowdfunding campaigns. 2) To adopt the multi-criteria decision method based on SAW into crowdfunding campaign valuation process. 3) To test the model applicability and to evaluate two crowdfunding campaigns. The paper is organized as follows: first, the literature review of financing crowdfunding campaigns and their success factors was conducted. Second, the applied methodology is described. Finally, the results, discussion, limitations and conclusions are discussed. 1. Literature review 1.1. Financing Crowdfunding Campaigns An ecosystem of crowdfunding consists of three groups: the platform, campaign owners and backers. The dominant point of every crowdfunding ecosystem is a platform. A platform is a technologically supported solution used to link supply (those who provide funds) and demand (those who are seeking for funds). The supply side consists of lenders, investors, backers and donors. The demand side consists of individuals and various organizations that seek for financial support (Jenik et al., 2017; Kumar et al., 2024). Crowdfunding has become very novel and popular financing application worldwide (Huang et al., 2023; Liang et al., 2019). First studies that emphasised crowdfunding platforms, compared the decision-making process of equity crowdfunding with new venture capital funding (Hagedorn & Pinkwart, 2016; Löher, 2017). However, there is not enough knowledge about the crowdfunding success targets should be evaluated. Additionally, studies on campaign success factors and investment criteria in equity crowdfunding is rare. On the other hand, knowledge of the crowdfunding success factors is required in order to better understand the dynamics of crowdfunding and its campaign success rates (Fan-Osuala et al., 2018). While the number of crowdfunding campaigns is Santautė VENSLAVIENĖ, Jekaterina KARTAŠOVA 377 increasing, it is essential to understand what motivates people to fund these campaigns. The success of crowdfunding campaigns is influenced by various factors, including social capital theory (Butticè et al., 2017; Colombo et al., 2015; Skirnevskiy et al., 2017), signal theory (Ahlers et al., 2015; Courtney et al., 2017), the herding effect (Mohammadi & Shafi, 2018), and local bias (Mendes-Da-Silva et al., 2016). Therefore, success factors for crowdfunding campaigns will be discussed from traditional funding, venture capital and business angels theories. Moreover, crowdfunding can be comparable with traditional e-commerce transactions (Ahlers et al., 2015). 1.2. Success Factors for Crowdfunding Campaigns Success factors for crowdfunding campaigns were taken from crowdfunding, venture capital and business angel theory and e-commerce literature. Most combinations of success factors were adapted from other study (Venslavienė et al., 2021) and are given in the table 1. According to the existing literature of crowdfunding theory, success factors are splitted into 4 categories: campaign characteristics, networks, understandability and quality signals (Cumming et al., 2020; Ferreira & Pereira, 2018). Those 4 categories included other sub- factors, in total counting 15 success factors from crowdfunding theory. While discussing Venture Capital and Business Angels theory, there were found 6 success factors (Huang et al., 2023; Liu et al., 2023; Zhu et al., 2023). Finally, from e-commerce theory, there were found three main factor groups related with risk, including 10 related risks. To summarize, 6 global factor groups including 24 success factors affect crowdfunding campaigns. Table 1. Success factors for crowdfunding campaigns found in the literature Theory Global Success Factor Codes Success factor Codes Description C ro w d fu n d in g t h eo ry Campaign characteristics C1 Campaign duration SC11 duration of the project campaign Funding target SC12 minimum sum needed to launch the project Min. Investment SC13 minimum amount to invest to participate in the project campaign Provision of financials SC14 financial forecasts/projections, early financial statements Number of early backers SC15 number of investors who invest before the campaign is launched Capital raised SC16 total capital raised for one project Number of investors SC17 actual number of investors investing in the same project Networks C2 Social media networks SC21 the followers’ social network of the project owner Private networks SC22 family and friends who support the project Understandability C3 Understandability SC31 is it oriented to business (B2B) or customer (B2C) Information about risk SC32 if the crowdfunding campaign is giving information about the risk VALUATION MODEL OF CROWDFUNDING CAMPAIGNS APPLYING SIMPLE ADDITIVE WEIGHTING METHOD 378 Environment commitments SC33 if the crowdfunding campaign is committed to the environment Quality signals C4 Updates SC41 how often updates are sent to audience Spelling mistakes SC42 are there any spelling errors in the campaign text Video SC43 is there a descriptive video about the campaign/product V en tu re C ap it al a n d B u si n es s A n g el s Company ratings C5 Team rating SC51 industry expertise educational background Experience the balance between team members’ skill sets perceived motivation, drive, passion, commitment, honesty Markets rating SC52 attainable market that determines the company’s growth potential. Concept rating SC53 how well the product fits the target market relevance of the end customer’s problem how well the company addresses the problem compared to other alternatives value of the solution to the customer Scalability rating SC54 it is easy to scale up the solution to the entire target market. Terms rating SC55 valuation whether the targeted funding amount is sufficient to lift the company to the next level Stage rating SC56 progress of the company on its development path remaining gap to the target state status of the product status of market validation existence of paying customers E -c o m m er ce t h eo ry Risk C6 Risks associated with the project SC61 product risk/funding object risk Social risk psychological risk post-funding risk/ repayment risk Risks associated with the project initiator SC62 project initiator risk/owner risk/seller risk time risk/convenience risk delivery risk Risks associated with the intermediary SC63 intermediary risk/privacy risk financial risk performance risk/operating risk Source: Adapted from (Venslavienė et al., 2021) These success factors should be used in evaluation model for crowdfunding campaigns. Santautė VENSLAVIENĖ, Jekaterina KARTAŠOVA 379 2. Methodology When assessing crowdfunding campaigns, investors usually do not have full information and have to turn their attention to secondary sources of information to help find out qualitative differences among crowdfunding campaigns. Thus, usually crowdfunding campaigns have both quantitative and qualitative success factors. Therefore, in order to create a model, six main factor groups were analyzed. Since these success factors are multidimensional, there is a need to apply methods that can link all criteria to one descriptive measure. Multi-criteria evaluation methods are the ones which can analyze those factors (Barretta et al., 2023; Hashemi et al., 2022; Khan et al., 2022). Multi-criteria decision making (MCDM) is applied to preferable decisions among available classified alternatives by multiple attributes (Taherdoost & Madanchian, 2023; Zavadskas et al., 2022). Multi-criteria desicion method is a method that does the analysis of several unrelated criteria. In this method environmental, economic, technological and social factors are discussed for the choice of the project and for making the choice sustainable (Alvarez et al., 2021). In this paper, Simple Additive Weighting method, one of MCDM methods, will be used to create valuation model. SAW method is the oldest, one of the simplest, widely known and practically used (Amalia & Alita, 2023; Kelen et al., 2023; Rusidah et al., 2023; Sinaga & Riandari, 2020). The criterion of the method Sj clearly demonstrates the main concept of multi- criteria evaluation methods – the integration of the criteria values and weights into a single magnitude (Amalia & Alita, 2023; Sinaga & Riandari, 2020). The sum Sj of the weighted normalized values of all the criteria is calculated for the j-th object: 𝑆𝑗 = ∑ 𝜔𝑖𝑟𝑖𝑗, 𝑚 𝑖=1 (1) Where ωi is weight of the i-th criterion rij is normalized i-th criterion’s value for j-th object; i = 1,..., m; j = 1,…, n; m is the number of the criteria used, n – is the number of the objects (alternatives) compared. The largest value of criterion Sj corresponds on the best alternative (Rajagukguk et al., 2022). All the compared alternatives must be ranked in the decreasing order of the calculated values of the criterion Sj. Adopting the SAW method in the crowdfunding campaign evaluation process several steps should be done: 1) Weights are given for each criterion as the importance of attribute 2) A value (score) is given for each alternative by criteria assessment 3) When there is already normalized matrix, every member of that matrix is multiplied by its weight and summed with other members of the alternative 4) The alternative with the highest score is selected. Model consists of three stages. First, choose criteria. Second, use SAW to weight the evaluative criteria and the last, third stage gives the optimal crowdfunding campaign to fund for investor. 3. Application of valuation model crowdfunding campaigns In order to have more specific and detailed valuation of factors, all factors were defined and grouped in smaller groups of sub-factors. Also, this way is easier for experts to evaluate VALUATION MODEL OF CROWDFUNDING CAMPAIGNS APPLYING SIMPLE ADDITIVE WEIGHTING METHOD 380 factors more precisely. After the expert evaluation, all sub-factors should be combined into factor groups with global weights and those factor groups will be used in the model to choose the most optimal crowdfunding campaign to invest in. When all factors are set, the factor weights can be found. Here expert estimates are chosen. This estimation is very subjective, therefore five professionals with experience in investing into crowdfunding platforms were chosen. Three of them constantly invest into crowdfunding campaigns, while the other two are the owners of crowdfunding campaigns. The results of expert evaluations are given in table 2. Table 2. Expert estimation of factor weights No Success factor Codes 1 2 3 4 5 Total Weights 1 Campaign duration SC11 8 3 2 4 1 18 0.036 2 Funding target SC12 4 4 3 3 1 15 0.030 3 Min. Investment SC13 10 5 3 2 1 21 0.042 4 Provision of financials SC14 3 3 2 3 5 16 0.032 5 Number of early backers SC15 0 4 4 2 8 18 0.036 6 Capital raised SC16 0 3 5 4 7 19 0.038 7 Number of investors SC17 0 3 3 2 6 14 0.028 8 Social media networks SC21 3 2 4 3 2 14 0.028 9 Private networks SC22 3 2 20 9 6 40 0.080 10 Understandability SC31 6 4 5 3 1 19 0.038 11 Information about risk SC32 10 3 1 2 4 20 0.040 12 Environment commitments SC33 5 2 1 3 3 14 0.028 13 Updates SC41 3 2 1 2 4 12 0.024 14 Spelling mistakes SC42 5 3 1 2 3 14 0.028 15 Video SC43 0 4 1 3 5 13 0.026 16 Team rating SC51 7 5 10 6 4 32 0.064 17 Markets rating SC52 5 3 2 3 4 17 0.034 18 Concept rating SC53 3 7 10 6 4 30 0.060 19 Scalability rating SC54 0 3 4 3 5 15 0.030 20 Terms rating SC55 0 5 4 3 2 14 0.028 21 Stage rating SC56 0 5 4 3 2 14 0.028 22 Risks associated with the project SC61 10 10 4 8 8 40 0.080 23 Risks associated with the project initiator SC62 5 8 3 11 5 32 0.064 24 Risks associated with the intermediary SC63 10 7 3 10 9 39 0.078 Total 100 100 100 100 100 500 1.000 Simple additive weighting method uses the typical normalization. The values of the criterion Sj of the method range from 0 to 1 for all the alternatives considered, while the sum of the criterion values is equal to unity allowing for graphical (geometrical) interpretation of the method. Santautė VENSLAVIENĖ, Jekaterina KARTAŠOVA 381 The global weights of each criterion should be estimated for further calculations. The global weights will show the most important factors from the whole group. The global weights are calculated in a very simple way – by finding simple arithmetic average from each sub- factor group. The results of global weights are found in table 3. The results shall be used in the valuation model to get which one of crowdfunding campaigns is more attractive to invest. The most important factors are related with Risk and with Networks, while the least important are quality signals. Table 3. Global weights of each factor group Global Success Factor Codes Success factor Codes Total Weights Global weights Campaign characteristics C1 0.0346 Campaign duration SC11 18 0.036 Funding target SC12 15 0.030 Min. Investment SC13 21 0.042 Provision of financials SC14 16 0.032 Number of early backers SC15 18 0.036 Capital raised SC16 19 0.038 Number of investors SC17 14 0.028 Networks C2 0.054 Social media networks SC21 14 0.028 Private networks SC22 40 0.080 Understandability C3 0.0353 Understandability SC31 19 0.038 Information about risk SC32 20 0.040 Environment commitments SC33 14 0.028 Quality signals C4 0.0260 Updates SC41 12 0.024 Spelling mistakes SC42 14 0.028 Video SC43 13 0.026 Company ratings C5 0.0407 Team rating SC51 32 0.064 Markets rating SC52 17 0.034 Concept rating SC53 30 0.060 Scalability rating SC54 15 0.030 Terms rating SC55 14 0.028 Stage rating SC56 14 0.028 Risk C6 0.0740 Risks associated with the project SC61 40 0.080 Risks associated with the project initiator SC62 32 0.064 Risks associated with the intermediary SC63 39 0.078 VALUATION MODEL OF CROWDFUNDING CAMPAIGNS APPLYING SIMPLE ADDITIVE WEIGHTING METHOD 382 The next step is to find out the most attractive crowdfunding campaign to invest in. There were analyzed two different crowdfunding campaigns from different sectors. The first crowdfunding campaign is in real estate and oriented to both foreign and local markets, while the second is innovative with unique product in the industry, but oriented only to local market. Moreover, both already have some early investors. Further, Crowdfunding Campaign 1 is considered to be on lower risk, while Crowdfunding Campaign 2 is the opposite – very risky. With proper descriptions of the crowdfunding campaigns, it is likely to assess crowdfunding campaigns by scores. In other words, the factor matrix should be normalised. As input data for calculation are the factors and their values of importance, the matrix should be normalised according to these conditions by evaluating the values of factors in the interval from 1 to 5, where: 1) Negative value of factors (decreasing value of factors). 2) Insufficient value of factors (remaining the same). 3) Medium value of factors (medium increasing). 4) Sufficient value of factors (sufficient increasing). 5) High value of criteria (high increasing). The normalized values of alternatives are provided in table 4. The estimation of aggregated values was done by applying the formula (1). The final results are presented in table 5. Based on the results, it is possible to draw some conclusions. As the optimal alternative, it should be selected the second crowdfunding campaign since its aggregated value is 1.0303 that is higher than the first crowdfunding campaign with aggregated value of 0.7982. Table 4. Global Normalized values for Crowdfunding campaigns Global Success Factor Codes Crowdfunding Campaign 1 Crowdfunding Campaign 2 Campaign characteristics C1 5 4 Networks C2 3 4 Understandability C3 4 3 Quality signals C4 2 3 Company ratings C5 3 3 Risk C6 2 5 Table 5. Crowdfunding campaign value calculation using SAW method Global Success Factor Codes Crowdfunding Campaign 1 Crowdfunding Campaign 2 Global weights Value of Crowdfunding Campaign 1 Value of Crowdfunding Campaign 2 Campaign characteristics C1 5 4 0.0346 0.1729 0.1383 Networks C2 3 4 0.0540 0.1620 0.2160 Understandability C3 4 3 0.0353 0.1413 0.1060 Quality signals C4 2 3 0.0260 0.0520 0.0780 Company ratings C5 3 3 0.0407 0.1220 0.1220 Risk C6 2 5 0.0740 0.1480 0.3700 Aggregated value 0.7982 1.0303 Santautė VENSLAVIENĖ, Jekaterina KARTAŠOVA 383 For this analysis six factor groups and 24 sub-factors were selected and 2 alternatives created. Multi-criteria evaluation method was applied to perform quantitative evaluation on these success factors. First, all values and weights of all factors were estimated and then they were applied to evaluation model. The overall conclusion from evaluation of those two alternatives shows not very wide dispersion, so it can be assumed that the factors and factor weights are selected correctly and the aggregated value sum of 1.0303 shows that alternative 2 is more attractive to choose for a decision considering the investment idea in some crowdfunding campaigns. DISCUSSION This paper provides the estimation framework to determine the optimal crowdfunding campaigns to invest. A new valuation model was proposed applying simple additive weighting methods which is part of multi-criteria evaluation method. The model suggests that crowdfunding investors should focus not only on traditional financial factors but also on their given parameters and conditions. The model works properly and helps for investors to decide on the best crowdfunding campaign. Moreover, it might be recommended to select more success factors or to use more combinations of other methods of multi-criteria evaluation to normalise the factors used and to pool the alternatives of various crowdfunding campaigns. The results from the implementation with more multi-criteria methods might show stronger and more effective results from different perspectives. CONCLUSIONS Before investing into new crowdfunding campaigns, investors must evaluate whether it is worth to invest or not. It is quite difficult to evaluate crowdfunding campaigns as most of them are very new in the market and there is little financial data. The valuation model to assess crowdfunding campaigns was proposed in this paper. Moreover, the multi-criteria valuation method simple additive weighting was applied. Comparing with other models, simple additive weighting is effective, as different factors can be chosen by different investor according to his personal preferences. For this analysis six factor groups and 24 sub-factors were selected and 2 alternatives created. Multi-criteria evaluation method was applied to perform quantitative evaluation on these success factors. First, all values and weights of all factors were estimated and then they were applied to evaluation model. Simple additive weighting method has worked properly and proved that it was the right method to apply in the model. The results of this method helped to choose the most optimal crowdfunding campaign to invest in. It can be concluded that the created model can be extensively applied for evaluating and selecting most optimal crowdfunding campaign. The overall conclusion from evaluation of those two alternatives shows not very wide dispersion, so it can be assumed that the factors and factor weights are selected correctly. VALUATION MODEL OF CROWDFUNDING CAMPAIGNS APPLYING SIMPLE ADDITIVE WEIGHTING METHOD 384 REFERENCES Ahlers, G. K. C., Cumming, D., Günther, C., & Schweizer, D. (2015). Signaling in Equity Crowdfunding. Entrepreneurship: Theory and Practice, 39(4), 955–980. https://doi.org/10.1111/etap.12157 Alvarez, P. A., Ishizaka, A., & Martínez, L. (2021). Multiple-criteria decision-making sorting methods: A survey. Expert Systems with Applications, 183, 115368. https://doi.org/10.1016/J.ESWA.2021.115368 Amalia, F. S., & Alita, D. (2023). Application of SAW Method in Decision Support System for Determination of Exemplary Students. Journal of Information Technology, Software Engineering and Computer Science, 1(1), 14–21. https://doi.org/10.58602/ITSECS.V1I1.9 Barretta, R., Taherdoost, H., & Madanchian, M. (2023). Multi-Criteria Decision Making (MCDM) Methods and Concepts. Encyclopedia 2023, Vol. 3, Pages 77-87, 3(1), 77–87. https://doi.org/10.3390/ENCYCLOPEDIA3010006 Butticè, V., Colombo, M. G., & Wright, M. (2017). Serial Crowdfunding, Social Capital, and Project Success. Entrepreneurship: Theory and Practice, 41(2), 183–207. https://doi.org/10.1111/etap.12271 Colombo, M. G., Franzoni, C., & Rossi-Lamastra, C. (2015). Internal social capital and the attraction of early contributions in crowdfunding. Entrepreneurship: Theory and Practice, 39(1), 75–100. https://doi.org/10.1111/etap.12118 Courtney, C., Dutta, S., & Li, Y. (2017). Resolving Information Asymmetry: Signaling, Endorsement, and Crowdfunding Success. Entrepreneurship: Theory and Practice, 41(2), 265–290. https://doi.org/10.1111/etap.12267 Cumming, D. J., Leboeuf, G., & Schwienbacher, A. (2020). Crowdfunding models: Keep-It- All vs. All-Or-Nothing. Financial Management, 49(2), 331–360. https://doi.org/10.1111/fima.12262 Fan-Osuala, O., Zantedeschi, D., & Jank, W. (2018). Using past contribution patterns to forecast fundraising outcomes in crowdfunding. International Journal of Forecasting, 34(1), 30–44. https://doi.org/10.1016/j.ijforecast.2017.07.003 Ferreira, F., & Pereira, L. (2018). Success factors in a reward and equity based crowdfunding campaign. 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), 1–8. Hagedorn, A., & Pinkwart, A. (2016). The financing process of equity-based crowdfunding: An empirical analysis. In FGF Studies in Small Business and Entrepreneurship (pp. 71– 85). Springer. https://doi.org/10.1007/978-3-319-18017-5_5 Hashemi, A., Dowlatshahi, M. B., & Nezamabadi-pour, H. (2022). Ensemble of feature selection algorithms: a multi-criteria decision-making approach. International Journal of Machine Learning and Cybernetics, 13(1), 49–69. https://doi.org/10.1007/S13042-021- 01347-Z/TABLES/15 Huang, X., Kabir, R., & Nguyen, T. N. (2023). Do project quality and founder information signals always matter? Evidence from equity and reward crowdfunding. International Journal of Finance & Economics. https://doi.org/10.1002/IJFE.2835 Santautė VENSLAVIENĖ, Jekaterina KARTAŠOVA 385 Hussain, N., Di Pietro, F., & Rosati, P. (2023). Crowdfunding for Social Entrepreneurship: A Systematic Review of the Literature. Journal of Social Entrepreneurship. https://doi.org/10.1080/19420676.2023.2236637 Jalal, A., Al Mubarak, M., & Durani, F. (2024). Financial Technology (Fintech). Studies in Systems, Decision and Control, 487, 525–536. https://doi.org/10.1007/978-3-031-35828- 9_45/COVER Jenik, I., Lyman, T., & Nava, A. (2017). Crowdfunding and Financial Inclusion. March. Kelen, Y. P. K., Sucipto, W., Tey Seran, K. J., Ullu, H. H., Manek, P., Lestari, A. K. D., & Fallo, K. (2023). Decision support system for the selection of new prospective students using the simple additive weighted (SAW) method. AIP Conference Proceedings, 2798(1). https://doi.org/10.1063/5.0154676/2904117 Khan, I., Pintelon, L., & Martin, H. (2022). The Application of Multicriteria Decision Analysis Methods in Health Care: A Literature Review. Medical Decision Making, 42(2), 262–274. https://doi.org/10.1177/0272989X211019040/ASSET/IMAGES/LARGE/10.1177_0272 989X211019040-FIG5.JPEG Kumar, J., Rani, M., Rani, G., & Rani, V. (2024). Crowdfunding adoption in emerging economies: insights for entrepreneurs and policymakers. Journal of Small Business and Enterprise Development, ahead-of-print(ahead-of-print). https://doi.org/10.1108/JSBED- 05-2023-0204/FULL/PDF Liang, T. P., Wu, S. P. J., & Huang, C. chi. (2019). Why funders invest in crowdfunding projects: Role of trust from the dual-process perspective. Information and Management, 56(1), 70–84. https://doi.org/10.1016/J.IM.2018.07.002 Liu, Z., Ben, S., & Zhang, R. (2023). Factors Affecting Crowdfunding Success. Journal of Computer Information Systems, 63(2), 241–256. https://doi.org/10.1080/08874417.2022.2052379 Löher, J. (2017). The interaction of equity crowdfunding platforms and ventures: an analysis of the preselection process. Venture Capital, 19(1–2), 51–74. https://doi.org/10.1080/13691066.2016.1252510 Mendes-Da-Silva, W., Rossoni, L., Conte, B. S., Gattaz, C. C., & Francisco, E. R. (2016). The impacts of fundraising periods and geographic distance on financing music production via crowdfunding in Brazil. Journal of Cultural Economics, 40(1), 75–99. https://doi.org/10.1007/s10824-015-9248-3 Mohammadi, A., & Shafi, K. (2018). Gender differences in the contribution patterns of equity- crowdfunding investors. Small Business Economics, 50(2), 275–287. https://doi.org/10.1007/s11187-016-9825-7 Mora-Cruz, A., & Palos-Sanchez, P. R. (2023). Crowdfunding platforms: a systematic literature review and a bibliometric analysis. International Entrepreneurship and Management Journal, 19(3), 1257–1288. https://doi.org/10.1007/S11365-023-00856- 3/FIGURES/12 Pandey, D. K., Hassan, M. K., Kumari, V., Zaied, Y. Ben, & Rai, V. K. (2024). Mapping the landscape of FinTech in banking and finance: A bibliometric review. Research in International Business and Finance, 67, 102116. https://doi.org/10.1016/J.RIBAF.2023.102116 VALUATION MODEL OF CROWDFUNDING CAMPAIGNS APPLYING SIMPLE ADDITIVE WEIGHTING METHOD 386 Rajagukguk, D. M., Manalu, M. R., Sihombing, M. J. T., & Panjaitan, M. I. (2022). DECISION SUPPORT SYSTEM FOR SELECTION OF ACHIEVEMENT TEACHERS TO GIVE AWARDS AT SMAS IMELDA MEDAN USING THE SAW METHOD. INFOKUM, 10(5), 747–754. https://doi.org/10.58471/INFOKUM.V10I5.1156 Rusidah, Risdianti, & Susanto, J. K. (2023). Selecting Favourite Majors at Sari Mulia University Using SAW Method. International Journal of Artificial Intelligence, 10(1), 1– 8. https://doi.org/10.36079/LAMINTANG.IJAI-01001.482 Sinaga, B. S., & Riandari, F. (2020). Implementation of Decision Support System for Determination of Employee Contract Extension Method Using SAW. Journal of Computer Networks, Architecture and High Performance Computing, 2(2), 183–186. https://doi.org/10.47709/CNAPC.V2I2.397 Skirnevskiy, V., Bendig, D., & Brettel, M. (2017). The Influence of Internal Social Capital on Serial Creators’ Success in Crowdfunding. Entrepreneurship: Theory and Practice, 41(2), 209–236. https://doi.org/10.1111/etap.12272 Taherdoost, H., & Madanchian, M. (2023). Multi-Criteria Decision Making (MCDM) Methods and Concepts. Encyclopedia 2023, Vol. 3, Pages 77-87, 3(1), 77–87. https://doi.org/10.3390/ENCYCLOPEDIA3010006 Venslavienė, S., Stankevičienė, J., & Vaiciukevičiūtė, A. (2021). Assessment of Successful Drivers of Crowdfunding Projects Based on Visual Analogue Scale Matrix for Criteria Weighting Method. Mathematics 2021, Vol. 9, Page 1590, 9(14), 1590. https://doi.org/10.3390/MATH9141590 Wan, X., Teng, Z., Li, Q., & Deveci, M. (2023). Blockchain technology empowers the crowdfunding decision-making of marine ranching. Expert Systems with Applications, 221, 119685. https://doi.org/10.1016/J.ESWA.2023.119685 Zavadskas, E. K., Lescauskiene, I., Juodagalviene, B., Bausys, R., & Keizikas, A. (2022). Comparison of the stair safety awareness in different target groups by applying the VASMA-C methodology. Archives of Civil and Mechanical Engineering, 22(4), 1–11. https://doi.org/10.1007/S43452-022-00487-5/FIGURES/4 Zhu, M., Zhou, W., & Duan, C. (2023). Integrating FMEA and fuzzy super-efficiency SBM for risk assessment of crowdfunding project investment. Soft Computing, 28(3), 2563– 2575. https://doi.org/10.1007/S00500-023-08534-W/FIGURES/3