Analysis of the variables affecting a startup’s success

Considering the growing importance of venture startups in the global economy, it appears to be crucial for them to pursue a sustained growth and competitiveness. It is noticeable the impact of online and mobile businesses, as well as the development of cultural, creative, content, and knowledge industries, which have diversified business models and glinted discussions on new interpretations. The occurrence of renewed combinations between technologies and markets is a pertinent matter. The design industry has extended in response to evolving business trends, offering new opportunities for design startups to explore additional value based on creativity and expertise. Further exploration into what makes design-based startups succeed is crucial. This emphasizes how important it is for entrepreneurs to act like entrepreneurs and think in certain ways to find opportunities.

One established method for evaluating success factors in social science research is the Analytic Hierarchy Process (AHP). This mathematical tool is adept at handling multi-objective decision-making, especially in situations characterized by uncertainty and multiple criteria. In the context of assessing the success of design startups, AHP stands out for its ability to identify key success factors and assign them weights.

In a recent study, a combination of Delphi analysis and AHP was employed. Experts were interviewed to discern key success factors, which were then evaluated using AHP to establish a criteria evaluation system for design startup businesses.

The critical success factors identified for design startup businesses are based on a framework derived from existing research on venture startup success. This framework is structured in a two-level architecture, with the first level encompassing four main success features: “entrepreneurship,” “innovation,” “technology,” and “economics,” each consisting of five attributes. In total, there are 20 success attributes in the framework. Additionally, factors such as sustainability, drawn from previous research on business venture success, are also considered essential traits for success.

The objective of the study was to ascertain the success factors of design startups in the Korean market and prioritize them based on their significance. To achieve this, a questionnaire was designed for empirical analysis. Quantitative analysis utilizing the AHP technique was employed to analyze the startup success factors.

The survey targeted individuals with professional experience in design or technology startup businesses, requiring a minimum of five years of relevant experience. Demographic data from the survey indicated that a significant portion of participants (87.5%) had over 10 years of experience in the field, with more than two-thirds (66.7%) being over 40 years old.

The weights assigned to different success factors were calculated using MS Excel software, with the questionnaires comprising 46 questions in total. The Consistency Ratio (CR) for all questionnaires was found to be less than 0.1, with individual values ranging from 0.0129 to 0.032. These values, falling below 0.1, indicate that the responses were both logically consistent and meaningful.

The priorities and weights of the factors in the proposed research models are summarized in Table 4. Local values denote the weights at each level, while global values are derived by multiplying these local values, which are then used to rank the evaluation areas and factors. The most crucial features, in descending order of importance, were Innovation (0.3999), Entrepreneurship (0.2442), Economics (0.1789), and Technology (0.1770).

Regarding second-level criteria under Innovation, Idea commercialization (0.3520) was deemed more critical than Market-oriented opportunity switch (0.2583), Progressive thinking (0.1655), Entrepreneurial motivation (0.1315), and Self-development (0.0924). This underscores the pivotal role of Idea commercialization and opportunity switch for the success of design startups.

In the realm of Entrepreneurship, Goal-orientation (0.3291) was considered more relevant than Entrepreneur’s competency (0.3076), Desire to accomplish (0.1462), Adventure tendency (0.1180), and Risk sensitivity (0.0990). This accentuates the significance of the leader’s goals and aptitude for success.

Regarding Technology, Creative technology utilization (0.3366) emerged as the most significant determinant compared to other factors such as Market-oriented technology, Intellectual property rights retention, Technical knowledge and craftsmanship, and High-technology globalization.

Within Economics, Continuous investment (0.6346) and Financial resource retention (0.1840) were identified as the most and least influential factors, respectively. Other factors in this criterion included Raising available funds, Raising venture funding, and Venture capital utilization.

A comparative analysis of the overall evaluation areas between design and technology startup groups revealed that Innovation was the most important area for both types of startup businesses. Additionally, both design (0.178) and technology groups (0.2735) commonly identified Economics as the third area of importance. However, for the design group, Entrepreneurship (0.2442) was deemed more important than Technology (0.1770). Conversely, the technology group indicated that Technology (0.2799) was more important than Entrepreneurship (0.1399). This finding demonstrates that design startups prioritize innovation ability and management skills within the fields of entrepreneurship and economics more than technological capability to achieve business success.

In addition to this study, numerous research endeavors aim to comprehend the reasons behind the success or failure of startups. One framework examines the characteristics of the individuals initiating the business, the structure of the organization they establish, the context they operate in, and the startup process itself. Success in entrepreneurship often correlates with personality traits such as a willingness to take risks, a sense of control, and a drive to achieve.

Further investigations delve into the motivations behind entrepreneurship. Studies have found that women who start businesses for personal reasons and men who start for external motivations tend to have better chances of success even before launching their business. Additionally, research explores factors contributing to the success of new tech businesses, the impact of startup experience on perceived expertise, and how entrepreneurs adapt to market demands.

In the realm of data analysis, machine learning techniques like classification and regression are utilized to forecast outcomes based on labeled datasets. The top 6 techniques for this analysis include:

• Lazy IB1: This algorithm delineates regions of instances instead of fixed clusters, offering greater flexibility.
• Random Forest: This approach amalgamates multiple decision trees to yield more precise predictions.
• Naive Bayes: These classifiers assume independence among features, rendering them effective for supervised learning.
• ADTree: ADTree organizes data into a graph structure, facilitating understandable predictions.
• Bayesian Network: This method examines network structures and probability tables to generate expectations.
• SimpleLogistic: It constructs logistic regression models by selecting pertinent attributes from the data.

The success or failure of startups hinges on various factors, such as initial funding, the duration required to secure funding, the number of funding rounds, and predictive features indicative of a company’s future performance. These include factors such as sustained customer interest, adept management, and astute financial planning. Negative factors also significantly influence the evaluation of startup sustainability.

Some key factors include:

• Seed Funding
• Time to Acquire Initial Resources
• Rounds of Funding: Referring to the number of funding rounds undergone by a company.
• Severity Factors: These factors are pivotal for the accuracy of prediction models and are commonly employed by institutions like S&P to evaluate companies’ capabilities.

These features are categorized into positive traits, such as strong traction, prudent financial management, early monetization strategies, networking prowess, and adaptability. Conversely, negative factors encompass aspects like lack of competitive research, incorrect market positioning, absence of a go-to-market strategy, and deficient leadership.

Negative aspects are rated on a scale of 1 to 5, where 1 represents lower severity and 5 denotes higher severity. Additionally, negative features are also assessed on a scale of -1 to -5, with -1 representing lower severity and -5 indicating higher severity. Examples of these ratings are illustrated in the respective tables.

In the selected study, researchers utilized statistical data sourced from CrunchBase, a comprehensive database containing information on thousands of companies. They employed the WEKA toolkit to develop predictive models, focusing on significant features such as seed funding amount, funding rounds, and severity factors. The study spanned from 1999 to 2014, examining companies’ responses to economic fluctuations.

Approximately 70 elements per company were considered in the analysis, with exclusions made for companies unable to increase assets or facing legal issues. Nine models, labeled M0 to M8, were developed, progressively incorporating additional factors. Six classification schemes, including Naive Bayes, AD Trees, BayesNet, LazyIB, RandomForest, and SimpleLogistic, were employed. Calculations were conducted using Leave-One-Out Cross Validation (LOOCV), with performance measured using metrics like area under the ROC curve (AUC).

The amount of funds raised, particularly seed funding, emerged as a crucial factor in the accuracy of predictive models. These models were then tested on real-world startups like Spotify, accurately predicting their success or failure based on funding amounts, severity scores, and burn rates. For instance, Spotify, with substantial funding and positive factors like traction and a solid customer base, was forecasted to succeed with an 88.9% probability. Conversely, Everpix, facing challenges such as poor management and a high burn rate, was predicted to fail with a 44.2% probability.

Overall, these models demonstrated success in predicting startup outcomes based on various key factors, offering valuable insights for both investors and entrepreneurs. The study employed various supervised learning classifiers, resulting in model accuracies ranging from 73.3% to 96.3%. Performance evaluation metrics such as the ROC area were used to assess the models.

Given the quality of predictions achieved, these models can serve as valuable tools for early-stage startups, providing insights into their potential outcomes and guiding them towards the most favorable path from inception. By learning from the failures of past startups and implementing appropriate strategies, entrepreneurs can enhance their chances of success.

Future research directions may include enhancing the accuracy and precision of models by incorporating additional severity factors. Moreover, there may be merit in developing a web-based tool based on the aforementioned approach, making it accessible and convenient for entrepreneurs and innovators to utilize in their decision-making processes.

Bibliography: Critical Success Factors of a Design Startup Business (Boyoung Kim , Hyojin Kim and Youngok Jeon) ,Econometric Estimation of the Factors That Influence Startup Success (Carlos Díaz-Santamari and Jacques Bulchand-Gidumal), Predicting the Outcome of Startups: Less Failure, More Success (Amar Krishna, Ankit Agrawal, Alok Choudhary)