case study on alternative tourism

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Ubc theses and dissertations, a critical review of alternative tourism: full fare tourism a case study of mundo maya johnston, alison m. --> -->.

Many planners concerned about the serious social and ecological impacts associated with the tourism industry now promote a shade of tourism called 'alternative' tourism. The difference between regular tourism and alternative tourism is that the latter has the connotation of being 'full fare' or sustainable. Generally speaking, alternative tourism is no less exploitive than regular tourism. The set of tourism activities now labelled as 'alternative' is merely a sub-component of the notorious mainstream tourism model. It unleashes the same type of negative social and ecological impacts as regular tourism, because the same planning methodology is employed. Mundo Maya, an alternative tourism program launched in 1990 but marketed before proper planning had taken place, follows this trend. The problems arising from the tourism industry's interpretation of alternative tourism points to a need to revisit the theory of alternative tourism and look to the 'success stories'. If present forms of alternative tourism are not sustainable, then it is vital that a line be drawn between tourism purporting to be alternative tourism and true alternative tourism. Otherwise a valuable body of theory could be discarded on the basis of misguided implementation efforts and opportunistic marketing. Within Mundo Maya, several small-scale independent success stories exist. These illustrate the conditions under which tourism can be 'full fare'. When the gap between the theory and practice of alternative tourism is closed, alternative tourism is a viable and rewarding community development tool.

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Tourism Alternatives

Potentials and problems in the development of tourism.

• Edited by: Valene L. Smith and William R. Eadington
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• Language: English
• Publisher: University of Pennsylvania Press
• Copyright year: 1992
• Edition: Reprint 2016
• Audience: College/higher education;
• Main content: 274
• Other: 6 illus.
• Keywords: Anthropology ; Folklore ; Linguistics ; Business ; Economics
• Published: November 11, 2016
• ISBN: 9781512807462

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FDA approves Neffy, a nasal spray alternative to an EpiPen that does not need a needle to stop an allergic reaction

Those at risk of a severe allergic reaction always have handy an EpiPen just in case. But soon their life might be saved by a spray in the nose rather than a needle to the thigh.

Last week the FDA approved Neffy, an epinephrine nasal spray, for use in emergency allergic reactions for kids and adults.

Retired allergist at the Northwest Asthma & Allergy Center Paul Williams said the spray “certainly has the potential” to become the preferred delivery of the drug over an autoinjector, commonly known as an EpiPen.

“A big advantage to this product is that it is not a shot. A lot of people don’t use their autoinjector out of fear of the needle, even when it could save their life. That is especially true for children with allergies who may be afraid of needles,” Williams said.

According to the FDA announcement, the nasal spray is the first epinephrine product that is not administered by injection.

“The availability of epinephrine nasal spray may reduce barriers to rapid treatment of anaphylaxis. As a result, Neffy provides an important treatment option and addresses an unmet need,” said FDA Center for Drug Evaluation and Research associate director Kelly Stone in a statement.

While most allergic reactions are minor and go away on their own, anaphylaxis is a severe allergic reaction that can be life threatening. These serious symptoms typically present themselves within minutes of exposure to certain foods, medications or insect stings that can cause anaphylaxis.

In these allergic reactions, the constriction of airways cause wheezing, shortness of breath and the inability for air to reach the lungs. Epinephrine relaxes these airways, reduces swelling and allows blood to flow normally. According to Williams, anaphylaxis is typically only fatal because of a delay of epinephrine being applied. For that reason, those at high risk of these reactions usually carry epinephrine in the form of a large autoinjector that is put into the thigh.

Neffy is sprayed into one nostril in a single dose – similar to other nasal sprays like Narcan. A second dose can be administered in the same nostril if symptoms worsen or don’t improve.

ARS Pharmaceuticals, the company that developed Neffy, said in a statement they hoped the nasal spray could be an alternative to those who want to avoid injecting epinephrine.

“A treatment alternative that avoids the need to inject epinephrine with a needle, which can be fraught with anxiety and fear for many – our team has worked tirelessly to create an easy-to-carry, easy-to-use, needle-free device that offers peace of mind to patients and caregivers by enabling them to administer epinephrine quickly and confidently when needed,” ARS Pharmaceuticals CEO Richard Lowenthal said in a statement.

According to the FDA, Neffy was approved based in the results of four studies of 175 healthy adults, which found epinephrine measured in the blood at adequate rates. Importantly, these test subjects were not in anaphylaxis when tested – a life-threatening emergency that cannot be observed in laboratory conditions.

For this reason Williams believes that for the time being Neffy should be seen as an addition to an EpiPen, rather than a replacement.

“At this point we don’t know for sure if any limitations from this drug will become apparent with actual anaphylactic cases,” he said - calling for the spray to be used as a “backup” to an autoinjector. “Hopefully we’ll get that experience over a one or two year period.”

One possible limitation is if anaphylaxis causes their nose to swell, Neffy may become less effective, Williams said. Neffy comes with a warning that certain nasal conditions, such as nasal polyps or a history of nasal surgery, may affect absorption.

It is often recommended those with severe allergies carry two EpiPens. Having an autoinjector and a nasal spray instead could be more convenient, he said.

“This spray is a somewhat smaller device, so it can more easily be carried by a lot of adolescents and older children who don’t always carry their EpiPens. Because it’s a little bit bulky, particularly if you have to carry two,” he said.

Neffy is approved for children and adults who weigh at least 66 pounds. Schools and many other public spaces that accommodate children have EpiPens in stock for emergencies. It remains unclear if Neffy could supplement schools’ autoinjector supply.

In a statement, a Washington Department of Health spokesperson said their agency would be announcing a new statewide standing order on epinephrine that includes Neffy. The new rules will make it easier for schools to obtain the drug, the statement read.

Spokane Public Schools has 1,194 students with an epinephrine plan on file. In a statement, Executive Director of School Support Services Rebecca Doughty welcomed the addition of Neffy as an option for schools.

“Our nurses are always prepared to utilize whatever medication the student’s provider has ordered and are comfortable doing so. A medication that can be delivered nasally instead of via needle will always be preferable for a child,” she said.

According to ARS Pharmaceutical, Neffy will be available within eight weeks of the FDA approval. With commercial insurance a prescription of two single-use Neffy devises should cost a $25 copay, according to the organizations. Without insurance Neffy will cost $199 for two doses. Each dose of Neffy lasts for 30 months.

Bridging the digital divide in Spokane County

There is a major challenge in cities all across Washington state, big and small.

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Produced water from the oil and gas industry as a resource—south kuwait as a case study.

1. Introduction

2. characteristics of pw from south kuwaiti oilfields and possible treatment methods for pw, 3. waste reclamation from oilfield pw, optimum utilization of pw and potential economic gains.

• All PW, separated from oil and gas, is gathered at one large gathering center for storage in the form of mega tanks.
• The distance between the large gathering center water storage tanks and the proposed site for the facility is 1 km. The chosen distance is similar to that in the plant design of the SK oilfield).
• PW is transferred entirely through pipelines.
• Pipelines are made from carbon steel.
• Water treatment, facility maintenance, facility operations, electricity, and disposal operations are included in the model operational cost.
• Water treatment costs include chemical additives and filtration costs.
• Pipelines and trucks are included in the model transportation cost, where the lease value of the trucks is embedded.
• Reinjection operations include all the costs associated with treatment operations, including chemical additives and filtration costs (for scenarios 1 and 3)
• Fifty percent of the treated water is sent to reinjection wells by pipeline and the rest is sold as treated water at the tipping value (scenario 3)
• There is a 15-percent oil production increase in the oilfield after water injection.
• The water cut in the produced liquid increases by 3 percent every year over the next 5 years. This estimate is based on the witnessed trend of an annual increase in the water cut in the SK oilfield [ 22 ]. Only 50 percent of the PW is to be reinjected (see also point 9).
• The oil output is steady after the initial increase.
• The new facility is an expansion to the current water management system.
• Treated water transportation costs by pipeline are USD 0.50 per barrel [ 58 ]. The costs of water transportation include all shipping of treated PW within the oilfield facilities up to the border of the oilfield area.
• The cost of injection for one barrel of water is USD 1–3 [ 59 ] (scenarios 1 and 3).
• We assume that no more disposal wells need to be drilled in the next 5 years.
• The treated-water tipping fee is constant throughout the 5-year period (scenarios 2 and 3).
• The cost of water transportation outside the oilfield is handled by the government authorities (scenarios 2 and 3).

4. Materials and Methods

5. results and discussion, expected impact of enhanced oil-recovery operations on kuwait’s economy, 6. conclusions and recommendations, supplementary materials, author contributions, data availability statement, conflicts of interest.

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Alsalem, F.; Thiemann, T. Produced Water from the Oil and Gas Industry as a Resource—South Kuwait as a Case Study. Resources 2024 , 13 , 118. https://doi.org/10.3390/resources13090118

Alsalem F, Thiemann T. Produced Water from the Oil and Gas Industry as a Resource—South Kuwait as a Case Study. Resources . 2024; 13(9):118. https://doi.org/10.3390/resources13090118

Alsalem, Feras, and Thies Thiemann. 2024. "Produced Water from the Oil and Gas Industry as a Resource—South Kuwait as a Case Study" Resources 13, no. 9: 118. https://doi.org/10.3390/resources13090118

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Constructing a Novel Network Structure Weighting Technique into the ANP Decision Support System for Optimal Alternative Evaluation: A Case Study on Crowdfunding Tokenization for Startup Financing

• Research Article
• Open access
• Published: 26 August 2024
• Volume 17 , article number  222 , ( 2024 )

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• Chun-Yueh Lin 1  

This study constructed a novel decision-making framework for startup companies to evaluate token financing options. A Network structure weighting (NSW) technique was developed and integrated with the analytic network process (ANP) to create a comprehensive assessment model. This innovative approach addressed the limitations of traditional multi-criteria decision-making methods by effectively capturing the complex interdependencies between factors influencing token financing decisions. The proposed model comprises three main steps: (1) utilizing a modified Delphi method to identify key factors affecting token financing, (2) developing the NSW technique to determine the network structure of these factors, and (3) integrating the NSW results into the ANP model to evaluate and rank the critical factors and alternatives. This study applied this framework to assess three token financing alternatives: Initial Coin Offerings (ICO), Initial Exchange Offerings (IEO), and Security Token Offerings (STO). The results indicate that STO is the optimal financing alternative for the analyzed startup scenario in token financing, followed by Initial Exchange Offerings and Initial Coin Offerings. The model identified platform fees, issuance costs, and financing success rate as the three most critical factors influencing the decision. This study contributes to both methodology and practice in FinTech decision-making. The NSW-ANP framework offers a more robust approach to modeling complex financial decisions, while the application to token financing provides valuable insights for startup companies navigating this emerging funding landscape. The proposed framework lays the groundwork for more informed and structured decision-making in the rapidly evolving field of cryptocurrency-based financing.

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1 Introduction

Due to the rise and development of Financial Technology (FinTech), as well as the enactment of the Jumpstart Our Business Startups (JOBS) in the U.S. [ 1 ], crowdfunding has become the newest financing means for enterprises in need of external funds [ 2 , 3 ]. In 2014, the total amount of funds raised through crowdfunding reached USD 16.2 billion, which was 167% higher than that of 2013 [ 4 ]. In addition, according to the statistical results of Statista Inc. (2020) [ 5 ], the total amount of alternative financing in 2020 was USD 6.1 billion, among which crowdfunding accounted for the largest market share. For this reason, it could be said that the development scale of crowdfunding in the global financial market has been rocketing.

Crowdfunding involves a number of different forms. The first form is donation-based crowdfunding, which mainly means to raise charity funds for the implementation of programs and projects. The second form is rewards-based crowdfunding, in which the investor can receive non-monetary rewards because of capital contributions. The third form is debt-based crowdfunding, in which the relevant interest arrangements between the investor and the fundraiser are determined in line with credit contracts. The fourth form is equity-based crowdfunding, in which the fundraiser uses the equities of the target company to exchange funds from the investor, while the investor receives such equities and therefore is entitled to that company’s revenues or dividends [ 6 , 7 , 8 ]. Estrin et al. [ 9 ] pointed out that equity-based crowdfunding depends mainly on the Internet or social network platforms. This fund-raising method not only reduces the transaction cost but also stands for a new business pattern under which startup companies can establish their own goodwill and provide investors with opportunities for investment. Although crowdfunding has many advantages for startup companies, risks do exist, including uncertainty of equity ownership, lack of liquidity, and damage to stockholder equity [ 10 , 11 , 12 ]. For this reason, past studies suggested that startup companies might obtain funds by offering tokens on the basis of distributed ledger technology and the immutability of blockchains. This not only could reduce the potential risks of traditional fundraising platforms but also could promote the transparency level of the relevant transactions [ 12 , 13 , 14 ]. Howell et al. [ 15 ] indicated that token financing has become one of the important sources for enterprises to raise funds through digital platforms. Presently, the development of crowdfunding tokenization mainly involves three patterns: (1) initial coin offerings (ICO), (2) initial exchange offerings (IEO), and (3) security token offerings (STO). ICO has the advantages of low cost and high speed. However, the risks of theft and fraud exist [ 15 , 16 , 17 ]. The advantages of IEO include having the business reputation of a third-party platform as a guarantee and handling the relevant transactions directly on the transaction platform. However, the possibility of the token price being manipulated cannot be ruled out [ 17 , 18 ]. The last pattern, STO, has the advantages of the highest level of safety and of being protected by the rules and regulations of regional governments. However, the high complexity of examination and verification as well as excessively low liquidity are problems that cannot be avoided [ 17 , 19 ]. The research results of past literature also show that for startup companies, the efficiency of token financing is higher than that of equity financing [ 20 ]. Furthermore, Chod et al. [ 14 ] pointed out that enterprises may take advantage of the decentralization features of token financing to make it more convenient for token investors in their project investments and reduce the cost of encouraging token investors to join the investment platforms. In this way, it is easier for entrepreneurs in raising funds.

For this reason, the utilization of token financing for the purpose of raising operation efficiency has become an important business strategy. The aforesaid three patterns of crowdfunding tokenization have their respective advantages and disadvantages, as well as potential risks. If startup companies intend to raise funds through virtual currencies, the alternatives of financing in cryptocurrency will affect the financing efficiency and lead to the capital turnover issue. Previous studies on token financing focused more on risk-return analysis [ 21 , 22 , 23 , 24 ], token rules and regulations [ 25 , 26 , 27 ], hedging of tokens [ 28 , 29 , 30 , 31 ], and prediction of price in tokens [ 32 , 33 , 34 , 35 ]. However, there is scarce evidence and a lack of applicable measurement tools in regard to assessing the optimal solution for the token financing of startup companies. Hence, algorithms for multiple criteria decision-making can be utilized for the construction of assessment models, so that the optimal solution for assessment can be reached [ 36 , 37 , 38 ]. Past studies also suggested that the optimal solution can be solved using the analytic hierarchy process (AHP) [ 38 , 39 , 40 , 41 , 42 ]. Although AHP can be used to assess the optimal solutions in different fields, it is unsuitable to use traditional AHP methods for decision-making problems in real situations. AHP is characterized by a hierarchical structure and based upon the presumption that the variables or criteria are independent from each other. Numerous problems relating to the assessment of optimal solutions and the relevant variables are correlated to or dependent on each other; as a result, complicated internal relationships cannot be solved through hierarchical or independent methods [ 43 , 44 ]. To solve this problem, Saaty [ 45 ] proposed the analytic network process (ANP), which added a feedback mechanism and interdependency to the AHP method to solve the problems of a lack of correlation and interdependency. ANP does not require the linear relationship of traditional AHP methods, which is top-down, and can establish an assessment pattern of networked relationships. Past literature has applied ANP models in the assessment of different industries, such as traffic problems [ 46 , 47 ], environment and energy assessment [ 48 , 49 , 50 ], filtration and selection of suppliers [ 51 , 52 , 53 ], and assessment of risk factors [ 54 , 55 , 56 ]. Thus, it can be seen that the problem of correlation or interdependency between criteria or variables cannot be solved effectively through AHP during decision-making, while ANP can effectively solve this problem. Although ANP can overcome the difficulties related to the presumption of independence in AHP, the ANP algorithm cannot ascertain the strength of the dependence and relationships between variables needed to generate a network structure. Previous studies addressing the network structure issue have applied deep machine learning concepts, as demonstrated by Moghaddasi et al., Gharehchopogh et al., and subsequent works by Moghaddasi et al. [ 57 , 58 , 59 , 60 , 61 ]. However, these studies primarily focused on the relationship in the Internet of Things, implicitly highlighting the challenges in applying such approaches to multi-criteria decision-making (MCDM) problems. Additionally, several studies employed the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to resolve network structures among criteria [ 62 , 63 , 64 , 65 , 66 ]. This approach offers an alternative perspective on capturing complex interrelationships within decision-making frameworks. However, the DEMATEL method has several limitations. First, the relationships derived through DEMATEL may be biased or misleading [ 67 , 68 ]. Additionally, the method faces convergence issues, as it cannot determine relationships between criteria when the data fail to converge [ 69 ]. As evident from Table  1 , there are two primary gaps in the existing literature. First, in terms of network structure methodology, while ANP, DEMATEL, and other decision-making frameworks have been proposed, they each have limitations. Second, regarding the research problem, while many studies have examined different aspects of token financing, there is a notable absence of comprehensive, quantitative decision-making frameworks specifically designed for startup companies evaluating token financing alternatives. In view of the above, this study developed a new network structure weighting (NSW) model, and then integrated NSW into ANP to remedy ANP’s shortcoming of being unable to determine the network structure. Finally, case studies were carried out to assess the optimal solution for startup companies engaging in token financing.

For the proposed NSW-ANP model, the modified Delphi method was utilized to determine the clusters and factors influencing startup companies engaging in token financing. Then, the network structure of these clusters and factors was determined based on the NSW method. Finally, the ANP model was utilized to calculate the weights of various factors and financing schemes for startup companies engaging in token financing and then sequence them to determine the optimal token financing schemes and their key factors. While ANP has been applied in various fields, this study proposed the first application of an enhanced ANP approach (integrated with NSW) to evaluate the token financing options for startups. This novel application demonstrates the versatility and effectiveness of our integrated approach in addressing complex FinTech decision-making scenarios.

This study makes significant contributions to the existing literature in both methodological innovations and novel applications. In terms of methodological advancements, we introduce a novel NSW technique that quantifies the strength of relationships between decision factors in a network structure. Furthermore, we develop an integrated NSW-ANP framework that enhances the capabilities of the traditional ANP by incorporating a more robust method for determining network relationships. With regard to novel applications, this study breaks new ground in two key areas. Firstly, we apply this integrated NSW-ANP framework to evaluate token financing options for startup companies, an area that has not been addressed using such a comprehensive decision-making approach. Secondly, this study provides the first systematic evaluation of ICO, IEO, and STO using a multi-criteria decision-making framework. This framework resolves the complex interdependencies between various factors, offering a more nuanced understanding of these emerging financing mechanisms. By combining methodological innovation with practical application in an emerging field, this study not only advances the theoretical understanding of multi-criteria decision-making processes but also provides valuable insights for practitioners in cryptocurrency-based startup financing. Academically, the new NSW-ANP model put forward in this study could be used for determining the network relationship of a research structure, and be integrated into the ANP to remedy the ANP’s shortcomings. The new integrated decision-making pattern put forward in this study also could provide valuable references for the measurement of the interdependency and correlation among variables in the assessment of the optimal solution of token financing for startup companies. Practically, the proposed framework could provide startup companies with a measurement tool containing a network structure and is valuable, so as to determine the optimal solution of token financing for startup companies introducing token financing to their businesses.

The remainder of this paper is organized as follows: Sect.  1 is the introduction, Sect.  2 describes the research method, Sect.  3 presents the case study, and Sect.  4 offers the conclusions.

2 Methodology

In this study, the clusters and factors were acquired through collecting experts’ opinions and literature reviews via modified Delphi method (MDM) as a first step. Next, the network structure of the clusters and factors was determined on the basis of the network structure weighting (NSW) method. Finally, the analytic network process (ANP) model was utilized to calculate and sequence the weightings of the various factors and financing schemes of startup companies engaging in token financing so that the most suitable token financing scheme and the key factors could be determined. The research method is presented in the following sections.

The Delphi method is an anonymous technique of decision-making by a group of experts. To solve a certain problem or find a solution for a particular future event, these experts are treated as the appraisal targets. For the final goal of reaching a stable group consensus among the experts, the group members are anonymous to each other, and particular procedures and repetitive steps are employed. The Delphi method attempts to combine the knowledge, opinions, and speculative abilities of experts in the field in an interruption-free environment. The Delphi method can be used to deduce what will happen in the future, effectively predict future trends, or reach a consensus over a certain issue [ 70 , 71 ]. This method is based upon the judgment of experts, and multiple rounds of opinion feedback are utilized to solve complicated decision-making problems. The traditional Delphi method emphasizes the following five basic principles [ 72 , 73 ]:

The principle of anonymity: All experts voice their opinions as individuals, and they remain anonymous when doing so.

Iteration: The questionnaire issuer gathers up the experts’ opinions and sends them to other experts. This step is carried out repeatedly.

Controlled feedback: In each round, the experts are required to answer pre-designed questionnaires, and the results are served as references for the next appraisal.

Statistical group responses: Comprehensive judgments are made only after the statistics of all the experts’ opinions are conducted.

Expert consensus: The ultimate goal is to reach a consensus after the experts’ opinions are consolidated.

The procedures of the Delphi method are as follows [ 74 ]:

Select the anonymous experts.

Carry out the first round of the questionnaire survey.

Carry out the second round of the questionnaire survey.

Carry out the third round of the questionnaire survey.

Consolidate the experts’ opinions and reach a consensus.

According to the modified Delphi method, Steps C and D are carried out repeatedly until a consensus is reached among the experts, and the number of experts should be between five and nine [ 75 , 76 ].

In this study, the experts’ opinions were gathered through the Delphi method and the relevant literature was discussed, so that the clusters and factors influencing startup companies engaging in token financing could be obtained.

2.2 NSW Model

This study utilized the Delphi method to collect the clusters and factors that could influence startup companies engaging in token financing schemes. In order to effectively carry out the calculation and assessment of ANP, the network structure of these clusters and factors need to be determined as a prerequisite for the subsequent filtration and selection of the optimal token financing scheme. Therefore, this study put forward the NSW method in order to acquire the relationships and the structure chart between clusters and factors. The NSW procedure is as follows:

Step 1: Collect and confirm the decision factors

The collection and confirmation of the decision factors can be realized through common tools such as literature reviews, the Delphi method, focus group interviews, and brainstorming. When decision-makers or experts need to determine n assessment factors that are consistent with the decision-making issues, the n assessment factors may be defined as \(\{ C_{1} ,C_{2} , \ldots ,C_{n} \}\) .

Step 2: Design the questionnaire

As far as the n factors determined by the decision makers or experts in Step 1 are concerned, a nine-point Likert scale can be utilized to ascertain the correlation and correlation strength between the factors. In the event of n factors, n ( n  − 1) comparisons in line with the scale need to be carried out.

Step 3: Calculate the weight of the network structure

Each expert compares and scores the decision factors. After that, all the comparison scores of the experts are used in the matrix construction and weighted calculation. The procedure is as follows:

2.2.1 Establish the Matrix of the Network Correlation and the Correlation Diagram

The correlation matrix is established as M , while \(\{ C_{1} ,C_{2} , \ldots ,C_{n} \}\) are the decision factors. If C i is influenced by C j , \(m_{ij}\) will be the scores of a quantitative judgment given by experts. On the contrary, if \(m_{ij} = 0\) , C i is not influenced by C j . The results can be shown in matrix M ( n  ×  n ) as follows:

The column aggregation and row aggregation of matrix M are:

\({\text{Column}}_{j}\) and \({\text{row}}_{i}\) , respectively, give the scores of factor j , which affects other factors, or factor j , which is influenced by other factors.

2.2.2 Define the Transition Probability Matrix

If transition matrix A is defined by the features of the Markov chain, A  = ( a ij ), as shown in Eq. ( 2 ). A is a regular Markov matrix, and the existence of stationary distribution \(x = \left( {x_{1} ,x_{2} , \ldots ,x_{N} } \right)^{T}\) satisfies Ax  =  x and \(\sum\nolimits_{i} {x_{i} = 1}\) . The characteristic value of 1 can be acquired through the characteristic vector corresponding to the characteristic value of Matrix A , or through the iteration method \(x^{0}\) , where \(x^{k + 1} = Ax^{k}\) , to obtain the characteristic value. x stands for the distribution of probabilities of the various factors being influenced when the transition number approaches infinity, and \(x_{i}\) stands for the network node score of the i th factor.

2.2.3 Calculate the Weightings of the Network Structure

According to the results described in II above, the network node score of each factor is distributed to the correlation diagram of each expert ( n experts have n correlation diagrams). Afterwards, based on the node score of factor i , the strength score of each expert’s factor i influencing other factor j goes through a standardized distribution using the correlation diagram to obtain each expert’s weighted value of the network structure, R, as shown in Eq. ( 3 ). In the end, the \(R(C_{i} ,C_{j} )\) of n experts is averaged and standardized, as shown in Eq. ( 4 ) and Eq. ( 5 ). The standardized results can then be integrated into the ANP model to assess the optimal token financing scheme for startup companies.

Saaty put forward ANP in 1996. This method is rendered through a network structure and derived from an ANP. Practically, there are many questions about decision-making assessment that are not limited to expressing their complex interrelated properties in a hierarchical and independent manner, and they are not of purely linear relationships either. Rather, these questions have a network-like structure [ 45 , 77 , 78 , 79 ]. Based on the original presumption and prerequisite of the analytic hierarchy process (AHP), Saaty [ 45 ] integrated relationship and feedback mechanisms into the AHP model to solve the problem of correlation between different principles.

Saaty pointed out that the relationships of interactive influence between clusters and elements can be analyzed in a graphic manner. Such relationships and interactive influence can be demonstrated through arrow lines [ 45 , 80 ], as shown in Fig.  1 . This network structure is crucial for understanding the fundamental difference between hierarchical and network-based decision-making models. Unlike traditional hierarchical structures, this network allows for complex interdependencies between different elements of the decision-making process. In Fig.  1 , the bidirectional arrows indicate that influence can flow both ways between clusters, reflecting real-world complexities where factors can mutually affect each other.

Source : Ref. [ 45 ]

The network structure.

According to the relationships and strengths of different factors in the aforesaid models and structure charts of ANP, a supermatrix is utilized for demonstration, as shown in Fig.  2 . This matrix is a critical component of the ANP, allowing for the quantification of relationships between all elements in the network. It is formed when the various clusters and respective factors contained in such clusters are listed on the left side and upper part of the matrix in an orderly manner. The supermatrix consists of a number of sub-matrices, which are formulated based on the eigenvectors after the comparison of different factors. In Fig.  2 , \(W_{11} ,W_{kk} , \ldots ,W_{nn}\) are the values of the eigenvectors after the comparisons and calculations.

Source : Refs. [ 45 ] [ 80 ]

The supermatrix of a network.

ANP is an algorithm based on AHP and can be divided into four steps. In Step 1, the structures are formed step by step. In Step 2, the questions are raised. In Step 3, comparisons of interdependent clusters are made in pairs and a supermatrix is formed. In Step 4, the ultimate choice and optimal scheme are selected [ 45 , 79 ].

This study apples the ANP as the foundation of our approach due to several key advantages it offers in the context of complex decision-making scenarios. First, it is well-suited for this application because it allows for the consideration of interdependencies and feedback relationships between decision factors, which is crucial in the dynamic and interconnected world of FinTech and token financing. Furthermore, it provides a structured approach to incorporating both qualitative and quantitative factors into the decision-making process. This is particularly beneficial when evaluating token financing options, as it allows us to consider both qualitative and quantitative data. Finally, it is able to prioritize alternatives based on a comprehensive set of criteria and sub-criteria. This is especially valuable when comparing different alternatives, each of which has its own unique set of characteristics and implications. ANP allows for a more comprehensive comparison than simpler decision-making tools. Among various MCDM techniques, the ANP has a superior capacity to model complex systems with intricate interdependencies. While other MCDM techniques, such as the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, offer effective means for ranking alternatives, they exhibit limitations in accounting for the multifaceted interrelationships among criteria.

Consequently, this study employs the ANP method as the foundation for constructing an integrated decision-making model. A brief introduction of the construction program of the network process pattern is as follows:

Step 1: Confirm the research problems and network structure

Determine the targets according to features of the problems and search for decision-making clusters, as well as the factors contained in the various clusters by employing the proposed NSW method to acquire the influencing strength of the various factors; finally, draw the network structure models of the decision-making problems according to the results of NSW.

Step 2: Create pair-wise comparison matrices and priority vectors

Compare the factors in pairs. This step has two parts: the comparison of clusters (in pairs) and the comparison of factors within clusters (in pairs). The comparison of factors within clusters (in pairs) can be divided into the comparison within a particular group and comparisons among different clusters. The assessment scale of the comparison is similar to that of AHP. In addition, the eigenvectors, which are reached through the various comparison matrices, serve as the values of the supermatrix, which can be used to illustrate the interdependency and relative significance among the clusters. Equation ( 6 ) can be utilized to calculate the scores of relative significance in regard to the various clusters and factors. As for the strength of the interdependency among the clusters and among the factors, NSW can be utilized to determine the network structure (as described in Sect.  2.2 .)

Step 3: Construct the supermatrix

The supermatrix can effectively solve problems related to the interdependency among the various clusters and factors within the system (as shown in Fig.  2 ). The values of the supermatrix consist of small matrices, which include the comparison of different factors (in pairs) and the comparison of interdependent factors (in pairs). The numerical values of clusters or factors without the influence of feedback are 0, as shown in Eq. ( 7 ). In this study, it was suggested that the overall network structure could be confirmed by NSW. For this reason, the NSW results were integrated into the supermatrix for subsequent assessment and to determine the strength of the interdependency in the supermatrix, as shown in Eq. ( 8 ).

The ANP calculation process includes three matrices: the unweighted supermatrix, the weighted supermatrix, and the limit supermatrix. The unweighted supermatrix stands for the weightings of the original results of the comparison in pairs. In the weighted supermatrix, the weighted values of a particular element within an unweighted matrix are multiplied by the weighted values of the relevant clusters. In the limit supermatrix, the weighted matrix multiplies itself repeatedly until a stable state is attained. According to ANP, if supermatrix W is in an irreducible state of stability, all columns in the supermatrix will have similar vectors, indicating convergence can be attained. The ultimate weighted values of each cluster, factor, and scheme can be calculated through Eq. ( 9 ) during the convergence process.

Step 4: Evaluate the optimal alternative

Through the ANP framework and the calculations of the unweighted supermatrix, weighted supermatrix, and limit supermatrix, all the alternative schemes, as well as the ultimate values of the groups and factors, can be attained in the limit supermatrix. The ultimate results of the weighted values are then ranked to determine the optimal scheme.

3 Case Study

This study aimed to establish the network structure weighting (NSW) model by integrating NSW into the analytic network process (ANP) and establishing an assessment pattern to analyze the optimal scheme of token financing for startup companies, as well as the weighted values of clusters and factors. The consolidation-type diagram of the analytical process is shown in Fig.  3 . This integrated framework is a key innovation, that employs the Modified Delphi Method to identify relevant factors, and applies the NSW technique to determine the network structure. The results are then integrated into the ANP model for final calculations and analysis. This integrated approach addresses the limitations of traditional ANP by providing a more robust and objective method for determining network relationships. It combines the strengths of expert knowledge (through the Delphi method), systematic relationship quantification (via NSW), and comprehensive decision analysis (through ANP), resulting in a more reliable and nuanced decision-making tool for token financing. First, the modified Delphi method was utilized to calculate the clusters and factors influencing startup companies engaging in token financing. Second, the network structure of the clusters and factors was determined on the basis of the NSW method put forward in this study. Finally, the weighted values of the network structure of NSW were integrated into the ANP model to calculate the weighted values for the various factors and various financing schemes of startup companies engaging in token financing. These weighted values were then sequenced to obtain the optimal scheme and key factors of token financing. Figure  4 presents the integrated framework for evaluating token financing options. This model incorporates five main clusters: Finance, Laws and Regulations, Risk, Investor, and Online Community, each containing several specific factors. The model also includes three token financing alternatives: ICO, IEO, and STO. This structure allows for a comprehensive evaluation of token financing alternatives, considering a wide range of relevant factors. By inclusion of diverse clusters including financial considerations, as well as legal, risk-related, investor-focused, and community aspects, the proposed framework allows startup companies to make well-informed decisions based on a thorough analysis of all relevant factors.

The integration processes

The research model

Step 1: Research the problem and confirm the decision factors

Past literature has pointed out that a research framework can be established only after experts reach a consensus on the factors [ 81 , 82 ]. Regarding the assessment of multiple principals, the number of selected experts should be between five and nine [ 76 ]. Therefore, this study included three scholars and four business starters, totaling seven experts. The goal of this study was to construct a consolidation-type pattern for the optimal scheme of token financing. Taking startup companies as examples, through a literature review and utilization of the Delphi method, a total of 17 factors, five clusters, and three token financing schemes were obtained, as shown in Fig.  4 . Relevant materials of each cluster and factors are shown as follows:

The definitions and illustrations of the clusters, factors, and token financing schemes in this study are as follows:

Finance: This includes issuance costs, platform fees, and transaction costs.

Issuance costs (C1) [ 83 , 84 ]: The costs of issuing tokens in different token financing schemes (for instance, Mint), which can vary.

Platform fees (C2) [ 83 ]: The costs for different token financing schemes to be launched on platforms (for instance, the costs for the schemes to be launched in Finance).

Transaction costs (C3) [ 83 ]: The transaction costs of different token financing schemes, which can vary (for instance, service charges).

Laws and regulations: This includes the place of issuance, government policy, token security regulations, and information disclosure transparency.

Place of issuance (C4): The laws, regulations, and rules of different countries and regions, as far as the issuance of tokens is concerned.

Policies (C5): The degree of support from government authorities on token financing.

Token security regulations (C6) [ 84 ]: The relevant policies on token security.

Information disclosure transparency (C7) [ 85 ]: Policies regarding the information disclosure of enterprises that issue tokens.

Risk: This includes financing schedules, token price fluctuations, reputation, shareholding proportion, and financing success rates.

Financing schedule (C8): The length of the financing scheme. For instance, Initial Coin Offerings (ICO) take a relatively long time, while Security Token Offerings (STO) take a relatively short time.

Token price fluctuations (C9) [ 83 ]: The price fluctuations of token transactions are obvious and influence relevant financing efficiency.

Reputation (C10) [ 86 ]: The degree of the token financing scheme’s requirements for the business reputation of the enterprises. For instance, ICO requires relatively less on the business reputation of the enterprises.

Shareholding proportion (C11): The proportion of shares corresponding to the tokens, which are held by the investors.

Financing success rates (C12) [ 87 ]: The success rates of different token financing schemes for enterprises.

The investor aspect: This includes the financing objects and financing thresholds.

Financing objects (C13): The investors being sought out by enterprises engaging in token financing. For instance, ICO and Initial Exchange Offerings (IEO) focus more on private investors, while STO focuses more on professional investors.

Financing thresholds (C14): The thresholds for enterprises to engage in token financing. For instance, the threshold of STO is relatively high.

The online community aspect: This includes the online sharing of voice, online public sentiment, and online trends.

Online sharing of voice (C15) [ 88 ]: The degree of influence of investors’ preferences of network volume in different financing platforms.

Online public sentiment (C16): The degree of influence of investor sentiment in the social network platforms of different financing platforms.

Online trends (C17): The degree of influence of the tendencies on the investors in the overall environment of token financing.

Token financing schemes: These include ICO, IEO, and STO.

ICO: The development, maintenance, and exchange for the purpose of financing, using blockchain technologies and virtual tokens.

IEO: The issuance and sales of tokens through the endorsement of exchanges. It also refers to the rules under which the exchanges are responsible for knowing your customer (KYC) compliance and anti-money laundering (AML).

STO: ICO is supervised by the government. It refers to the practice of linking the assets of enterprises to tokens through securitization, as well as the sales of such assets.

Step 2: Develop the network structure models through NSW

The results acquired in Step 1 were integrated into the NSW models suggested by this study, so as to determine the network structure. The relevant procedures are as follows:

Step 2.1: Design the questionnaire

In regards to the five clusters and 17 factors obtained by the experts in Step 1, a nine-point Likert scale was utilized to determine the strength of correlation between different factors. In the event of n factors, n ( n  − 1) comparisons of the scale were carried out. Because this study referred to seven experts for the development of the network structure model, the data involved were quite complicated. The NSW procedures were illustrated in accordance with the finance clusters, as well as the three factors of issuance costs, platform fees, and transaction costs. The questionnaire design for the finance clusters is shown in Table  2 , in which 0 indicates no influence was observed, while 9 indicates the influence was of the highest level. The strength of correlation among the three factors of finance obtained through the questionnaires of the seven experts is shown in Fig.  5 . Each expert’s assessment is represented in a separate diagram, allowing for a comparison of individual perspectives. The differences in experts’ opinions highlight the subjective nature of these assessments and underscore the importance of aggregating their opinions. The generally strong correlations between factors, particularly between issuance costs and platform fees, suggest that these financial aspects are closely interrelated in token financing decisions. This visualization is crucial for understanding the foundation of our network structure, as it forms the basis for our NSW calculations.

The strength of correlation among the three factors of finance obtained through the questionnaires of the seven experts

Step 2.2: Calculate the weight of the network structure

Each expert compared the factors and scored them in terms of strength. After that, the comparison scores provided by the experts were used in the construction of the matrices and weighted calculations. First, the correlation matrices of the finance clusters, M 1 to M 7 , were established on the basis of Eq. ( 1 ) and the scores of the strength given by the seven experts, as shown below. Second, correlation matrix M was transformed into probability matrices A 1 to A 7 through Eq. ( 2 ), as shown below, and the iteration method was used n times to obtain the characteristic values (eigenvalues) of each questionnaire and factor. Third, this study calculated the weighted values of the correlation among C 1 , C 2 , and C 3 , as well as R ( C i , C j ) 1 to R ( C i , C j ) 7 , through Eq. ( 3 ), as shown in Fig.  6 . This visualization is crucial for understanding how individual expert opinions contribute to the overall network structure. The variation in weights across experts highlights the subjective nature of these assessments and the necessity to aggregate multiple expert opinions. Notably, most experts consistently assign higher weights to the relationships between issuance costs ( C 1 ) and platform fees ( C 2 ), indicating a strong perceived connection between these two factors. In the end, the ultimate weighted values of the network structure (the scores of the correlation degree) were calculated using Eq. ( 4 ) and Eq. ( 5 ). The weighted values of the network structure of the various clusters and factors are shown in Fig.  7 . Figure  7 illustrates the final network structure weights for all five clusters and their respective factors, which is the foundation for our subsequent ANP analysis. These network structure weights provide a comprehensive understanding of the relative importance and interconnectedness of various factors in token financing decisions. They serve as a crucial input for our ANP model, ensuring that the final decision-making process accurately reflects the complex realities of token financing.

The network structure weights of finance cluster’s factors by 7 experts

The network structure weights of five cluster’s factors

Upon completing the calculations, the results of the weighted values for the network structure were integrated into the ANP models to establish the comparison matrices and calculate the eigenvectors.

Step 3: Perform pair-wise comparisons of the matrices and priority vectors

The eigenvectors of the clusters and factors were calculated through the AHP processes and pairwise comparison of features of matrices. The eigenvectors of the degree of correlation between different clusters and factors were calculated through NSW. The cases in this study involved five clusters (finance, laws and regulations, risk, investor, and online community), 17 factors (issuance costs, platform fees, transaction costs, place of issuance, government policy, token security regulations, information disclosure transparency, financing schedules, token price fluctuations, reputation, shareholding proportion, financing success rates, financing objects, financing thresholds, online share of voice, online public sentiment, and online trends), as well as three schemes.

The comparison matrices (in pairs) and the geometric method were utilized to calculate the eigenvectors, while the eigenvectors for the network structure of the correlation strength scores were obtained on the basis of NSW. The eigenvectors obtained for the various comparison matrices, as well as the eigenvectors related to the correlation strength of the factors, served as the values of the supermatrix, which was used to illustrate the correlation strength and the relative importance of different clusters. The clusters might confirm the eigenvectors of the network structure through NSW, and the scores of the relative importance were calculated using Eq. ( 6 ). The results of the eigenvectors for the network structure of the various factors are shown in Step 2.2, and the comparison matrices (in pairs) and the weighted values of the five clusters are shown in Table  3 . Table 4 contains the scores for the relative importance of the various factors against the alternative schemes. In this study, Super Decision V2.0 (software) was utilized for the subsequent assessment of the ANP models. The eigenvectors of the network structure obtained through the NSW were inputted into Super Decision V2.0 to integrate NSW and ANP and assess the optimal scheme and the key factors.

Step 4: Construct the supermatrix

The eigenvectors of the relationships among the factors, as well as the eigenvectors regarding the weights of the factors to the schemes, were determined according to the results of Step 3. In Step 4, a supermatrix is established on the basis of the eigenvectors obtained in Step 3, so that the optimal scheme for startup companies engaging in token financing could be measured. During the ANP process, the ultimate weighted values of the various factors and schemes were calculated through the unweighted supermatrix, the weighted supermatrix, and the limit supermatrix. First, the calculated eigenvectors of the NSW model for the factors and pair-wise comparison matrices were utilized to establish the unweighted supermatrix. Second, the unweighted supermatrix was multiplied by the reciprocals of the weighted values of the relevant clusters to generate the weighted supermatrix. Finally, the results of the weighted supermatrix were multiplied by themselves repeatedly until a stable probability distribution was realized. This probability distribution reflected the ultimate weighted values to be reached. The various supermatrices are shown in Tables 5 , 6 , and 7 .

Step 5: Evaluate the optimal alternative

Through the supermatrix mentioned in Step 4, as well as the operation of Super Decision, the ultimate weighted values of the various factors and schemes under the consolidated NSW network structure could be obtained, as shown in Table  8 .

This study suggested the establishment of a set of network assessment procedures integrating the new NSW technique with the ANP model, in order to analyze the optimal scheme for startup companies engaging in token financing. The findings indicated a number of results. The sequence of the weighted values for the five clusters was as follows: finance (0.307) > risk (0.294) > laws and regulations (0.211) > investors (0.106) > online community (0.082). In addition, the sequence of the weighted values for the factors was as follows: platform fees (0.083) > issuance costs (0.078) > financing success rate (0.053) > government policy (0.0049) = financing schedule (0.049) > transaction costs (0.044) > financing threshold (0.040) > information disclosure transparency (0.039) > token price fluctuations (0.032) = shareholding proportion (0.032) > financing object (0.031) > reputation (0.030) > place of issuance (0.027) > token security regulations (0.026) > online share of voice (0.022) > online public sentiment (0.019) > online trend (0.014). Finally, the sequence of the optimal scheme for startup companies engaging in token financing is as follows: ICO (0.057) > IEO (0.101) > STO (0.175). STO is the optimal scheme for startup companies to engage in token financing.

4 Conclusion and Future Work

4.1 conclusion.

The rapid development of FinTech has become one of the goals of inclusive financing. Fintech, which depends on information technology to find solutions in the financial field, is becoming the mainstream future trend in the financial industry, especially in the development of new business patterns. Startup companies might find it difficult to borrow money from traditional financial institutions due to their business operation features and financial structures. For this reason, alternative financing has gradually become an important channel for startup companies to acquire financing. Token financing is a relatively new business pattern in the field of alternative financing, and it can avoid the shortcomings and problems of crowdfunding.

However, the development history of token financing is diversified and complicated. Previous studies in this field focused more on the analysis of the values of virtual currencies. Generally speaking, when startup companies are faced with the option of token financing, which is a new business pattern, they have relatively little information available for business assessments and decision making. When startup companies assess the optimal scheme for token financing, they often use multi-principle decision-making models, which can solve the problems of filtration and selection in token financing. However, multi-principle decision-making models depend heavily on the presumption that the variables (or criteria) are independent from each other. Therefore, such models might not be suitable for the assessment of decision-making problems in the real world.

ANP can be used to solve the problem of independence assumption in traditional multi-principle decision-making models. Although ANP can overcome the problem of independence assumption, it is still unable to ascertain the strength of the dependence and relationships between variables before producing a network structure. In this study, a new model, NSW, was put forward. This new model could be used to calculate the correlation between variables and generate the network structure. In addition, NSW could be integrated into ANP to generate the network structure. In the end, the assessment of the optimal scheme for startup companies engaging in token financing served as the case study. The results of this study show that finance is the most critical cluster in the assessment aspect. In other words, when startup companies intend to engage in token financing, financial issue is the first aspect to be considered. Token financing is the most up-to-date financing method in the era of FinTech, and capital turnover and financial structure are key issues during the development of startup companies. The sequence of key factors are platform fees, issuance costs, and financing success rate. Moreover, this sequence suggests that when startup companies intend to engage in token financing, the key factors are the aspect of costs and the success rate of financing. Finally, the optimal scheme for startup companies engaging in token financing is STO. After considering financial issues, costs, and relevant risks, startup companies should, based on the cost assessment and the success rate of financing, adopt STO for token financing to promote the financial efficiency of such companies.

This study proposed the NSW technique as a novel tool for validating network structures in decision-making processes and integrated NSW into the ANP model to develop a comprehensive framework for evaluating optimal token financing strategies. The contributions of this study in token-based financing include both methodological advancement and practical application. In terms of methodology, this study integrated the NSW technique with the ANP to enhance the robustness of existing frameworks in capturing complex interrelationships within decision-making processes. This innovative approach addresses limitations in traditional methods by providing a more comprehensive quantification of the strength and directionality of relationships between decision factors. As for practical application, this study presents the first comprehensive evaluation of token financing options for startup companies utilizing this advanced decision-making approach. The integrated NSW-ANP framework can be applied to ICO, IEO, and STO, thus offering valuable options for cryptocurrency-based startup financing. This systematic evaluation considers the intricate interdependencies among various factors influencing the selection of optimal financing strategies. By bridging the gap between theoretical innovation and practical implementation, this study not only advances the field of multi-criteria decision-making but also provides startup entrepreneurs and investors with a sophisticated tool for token-based financing options. Academically, this study provided a new NSW technique, as well as the application procedures to integrate NSW into ANP. This study also presented a case study of the assessment of the optimal scheme for startup companies engaging in token financing. Practically, this new framework could provide entrepreneurs of startup companies with valuable measurement tools for promoting their company’s capital turnover rate through token financing under the rapid development of FinTech.

4.2 Limitation and Future Research

While acknowledging the substantial advantages offered by our integrated framework, it is imperative to recognize its inherent limitations. The following constraints warrant further investigation and potential mitigation in future research:

The potential complexity and mathematical technique of the proposed model, which might make it challenging to implement for organizations.

The static nature of the model, which may not fully capture the decision risks of uncertainty in the cryptocurrency and token financing landscape.

At the current stage of development, the model may not comprehensively capture the effects of factor weight variations on the rankings of alternatives.

After discussing these limitations, we will outline potential directions for future research. This section will propose several avenues for extending and refining our work:

Expanding the application of the NSW-ANP method to other areas of FinTech decision-making beyond token financing.

Integration of fuzzy set theory into the NSW-ANP model to address decision uncertainty risks.

A sensitivity analysis was conducted to ascertain the effects of factor weight variations on the rankings of alternatives.

Data Availability

Not applicable.

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Lin, CY. Constructing a Novel Network Structure Weighting Technique into the ANP Decision Support System for Optimal Alternative Evaluation: A Case Study on Crowdfunding Tokenization for Startup Financing. Int J Comput Intell Syst 17 , 222 (2024). https://doi.org/10.1007/s44196-024-00643-0

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DOI : https://doi.org/10.1007/s44196-024-00643-0

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