Interdisciplinarity in Tech Startups Development – Case Study of ‘Unistartapp’ Project

Kopera, Sebastian; Wszendybył-Skulska, Ewa; Cebulak, Joanna; Grabowski, Sebastian

doi:10.2478/fman-2018-0003

Cited by 16 publications

(12 citation statements)

References 5 publications

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“…Sentiment Analysis is a research methodology that analyzes the feelings of a given sample, which normally comes from digital environments such as online platforms or social networks, to find the different opinions with different methodological approaches. It has been confirmed that Sentiment Analysis can identify the feelings and therefore the opinions of product users in order to understand how these feelings and opinions affect the users' decision making [3,8]. There are different options and approaches for this technique.…”

Section: Sentiment Analysis With Social Network Analysismentioning

confidence: 99%

“…A startup is a technology-based company that offers a new product or service using the added value of the incorporated technology. This is defined as 'innovation through technology' [8].…”

Section: Introductionmentioning

confidence: 99%

“…The product or service is launched when the product is ready, and many startups create successful products and services for consumers whose consumption habits are based on the digital age. Examples of services and products that were created as startups are WhatsApp, Facebook, Instagram, and the technological giant Alphabet (Google) [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining

2019

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The main aim of this study is to identify the key factors in User Generated Content (UGC) on the Twitter social network for the creation of successful startups, as well as to identify factors for sustainable startups and business models. New technologies were used in the proposed research methodology to identify the key factors for the success of startup projects. First, a Latent Dirichlet Allocation (LDA) model was used, which is a state-of-the-art thematic modeling tool that works in Python and determines the database topic by analyzing tweets for the #Startups hashtag on Twitter (n = 35.401 tweets). Secondly, a Sentiment Analysis was performed with a Supervised Vector Machine (SVM) algorithm that works with Machine Learning in Python. This was applied to the LDA results to divide the identified startup topics into negative, positive, and neutral sentiments. Thirdly, a Textual Analysis was carried out on the topics in each sentiment with Text Data Mining techniques using Nvivo software. This research has detected that the topics with positive feelings for the identification of key factors for the startup business success are startup tools, technology-based startup, the attitude of the founders, and the startup methodology development. The negative topics are the frameworks and programming languages, type of job offers, and the business angels’ requirements. The identified neutral topics are the development of the business plan, the type of startup project, and the incubator’s and startup’s geolocation. The limitations of the investigation are the number of tweets in the analyzed sample and the limited time horizon. Future lines of research could improve the methodology used to determine key factors for the creation of successful startups and could also study sustainable issues.

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Section: Sentiment Analysis With Social Network Analysismentioning

confidence: 99%

“…A startup is a technology-based company that offers a new product or service using the added value of the incorporated technology. This is defined as 'innovation through technology' [8].…”

Section: Introductionmentioning

confidence: 99%

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Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining

2019

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“…As regards interdisciplinary interventions between the health and technological fields, interest has been shown on numerous occasions: monitoring of the diabetic patient [37], repair and reproduction of human cells and tissues by 3D bio-printing [38] or the implementation of telemedicine as a measure to reduce infections in the COVID-19 pandemic [39]. In reference to interventions carried out in the university educational field, evidence has been found of interdisciplinary work among students in the technological and economic fields for the development of Apps [40], and between students of Biotechnology and students of Business Administration to evaluate the economic viability of innovative projects in the change of biotechnology [41]. Furthermore, the study by Pawar et al in 2017 concluded that multidisciplinary simulation education offers a positive emotional response from students and prepares them for relevant learning, dynamic thinking and a willingness to continue learning [42].…”

Section: Student Network Position and Academic Performance Results (Imentioning

confidence: 99%

Networks and Emotions in Cooperative Work: A Quasi-Experimental Study in University Nursing and Computer Engineering Students

et al. 2020

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University students establish networks that impact on their behavior. Social Network Analysis (SNA) allows us to analyze the reticular structures formed and find patterns of interaction between university students. The main objective of this study was to observe the impact of interdisciplinary collaborative work between nursing and computer engineering students on the collaboration and friendship networks, emotions and performance of the participants. It is a quasi-experimental descriptive study with pre- and post-intervention measurements. The contact networks analyzed showed an increase in density in the post-intervention period. The most central people in the network corresponded with those who considered positive emotions most in their academic environment, while the less central people coincided with those who highlighted negative emotions. Academic performance was only significantly associated in the collaboration network, between this and OutdegreeN. This study shows the impact of interdisciplinary activities on teaching methodologies and the repercussions of emotions on curricular activity.

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“…Davila et al (2015) draw attention that the start-up enters an established market with a business model that ex-post turns out to be economically nonviable. Kopera et al (2018) explain that a source of success is rarely a technological superiority alone but rather a business model that stays behind. The business model supports the complex business endeavor, resulting in better product design and its business model configuration.…”

Section: Introductionmentioning

confidence: 99%

The Business Model of Start-Up—Structure and Consequences

Slávik

2019

Administrative Sciences

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A start-up already at its origin has to solve the crucial existential condition that is a viable business model. The purpose of the research is to deepen and expand the knowledge about the structure of the start-up business model and its impact on entrepreneurial performance. Field research was carried out in three stages over one and a half years. The source of knowledge about the studied start-ups has been the personal experience recorded in the questionnaire. Start-ups are small closed communities that do not sufficiently shape external partnerships, cannot reach the customers, although they can identify them well enough, try to improve the business model blocks but they do not pay attention to their coordination and perceive the monetization in a simplified way. The impact of the business model on start-up performance has been confirmed but the internal blocks affect business performance unambiguously over the three stages of the research. The best conversion of users to customers and subsequently to revenues are distinctive for the start-ups with their own simple distribution channel and partners who are experienced distributors. The lessons learned can directly improve the results of start-ups when their incompleteness and imperfection will be substituted by appropriate partners. Start-ups provide a space for the new jobs and the self-realization of ambitious people with a sense of service for society. Empirical research on start-ups identifies their weaknesses and possibilities to increase their entrepreneurial performance.

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Interdisciplinarity in Tech Startups Development – Case Study of ‘Unistartapp’ Project

Cited by 16 publications

References 5 publications

Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining

Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining

Networks and Emotions in Cooperative Work: A Quasi-Experimental Study in University Nursing and Computer Engineering Students

The Business Model of Start-Up—Structure and Consequences

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