Much research has studied how a company can maximize its profit. Relatively small number of them focused on Value Proposition, though the number of authors proved that companies that deliver multiple values experience better business performance. This article describes a current research on linguistic analysis of startups in the context of the air transport industry. Analyzing startups manually is a very time consuming task, so the automation of the process would be beneficial. The author takes corpus linguistic approach, created an experiment protocol and is on the stage of conducting an experiment. Under this experiment air transportation startups' landing pages were collected in the number of 800. 100 annotators first were preliminary surveyed and then trained to annotate startups. Post-annotation training will be conducted to understand the difference in expertise level. The annotation results will be further analyzed and linguistic features and patterns will be identified. As a result of the research, an author will develop a methodology for analysis of values based on a model of automatic identification of values in the text of a startup's landing page in the air transportation industry.
Value chain analysis is a primary corporate strategy tool. Traditionally relatively few
values have been pursued; price, quality, etc. Recently, customers are demanding a
wide variety of values. Most importantly, some of the values that have previously
received little attention become dominant values (concept known as value shift).
Some examples: cars - eco-friendliness, electronics – usability, food - fairness,
organic. As new values become prominent, industries and their companies will be
transformed; not following values could result in wasted resources (pursuing the wrong
values) and becoming irrelevant (not pursuing the right ones). Hence, the ability to
identify values early on is important for business performance. Startups are often the
first to discover new values. As an example, Skytran, startup from air transportation
field, offers autonomous, zero-emission vehicles arrowing above congested streets.
The company’s landing page identifies its values as “high speed, high capacity, low
cost” [
The main research question is tools and procedures for analysis of value proposition of startups in the area of air transportation from their textual description in the frame of developing a methodology for analysis of values based on a model of automatic identification of values in the text of a startup’s landing page in the air transportation industry.
The initial contribution of this research is the development of methodology of analyzing values within industry based on startups value propositions. The procedures and tools integrated in the model are either articulated or developed by the author throughout the Thesis.
The approach. The author uses a Natural Language Processing (NLP) approach to identify the methods and features well suited for this problem. A bottom-up (datadriven) technique is taken, i.e. the author first constructs the dataset and then analyzes it using both computational and linguistic methods to identify which features and methods perform the best. The author also takes the corpus linguistic approach, i.e. reliable language analysis is more feasible with corpora collected in the field in its natural context.
The object of the research is startups’ landing pages in the field of air transport industry.
The subject of the research is value proposition of a startup.
The tasks to be performed are the following: 1. To study theoretical literature on the 5. To identify features (e.g. linguistic, research topic semantic) 2. To examine existing research on the 6. To identify patterns
research topic 7. To develop testable predictions 3. To collect preliminary data 8. To build a model 4. To conduct an experiment 9. To test a model 2
The research methodology is presented in Figure 1. The author takes data-driven
scientific method. The general idea was adopted from the paper describing the scope
of big data in medicine [
With the accessibility of large datasets and advanced correlation/statistical tools, will we still need to rely on hypotheses in scientific research? Traditionalists say that in purely data-driven methods, one may not realize where to look for those riveting findings if no hypotheses were formed beforehand.
Big data advocates, on the other side, propose that with no prior beliefs, one is not
driven by established ways of thinking or creating, opening the possibilities of
breakthrough insights where nobody had been before [
Research Theoretical Framework. The following theories and methods have
been used in work: the system approach [
Limitations of the research are linked to the research base: one industry for startups - aviation industry, one hundred experiment participants and their level of expertise (undergraduate students, non-native English speakers), task complexity and ambiguity, ambiguity of natural language.
Scientific novelty:
analysis of Value Proposition in the context of air transportation and startups;
construction of Value Proposition corpus / dataset; systematization of Value Proposition
(ontology); analysis of linguistic patterns of Value Proposition; automation of
linguistic features detection that could be used to detect Value Proposition (feature
engineering [
Articles reviewed here and referenced throughout the article were sourced from online libraries using a relative research approach: papers related to Value Proposition, startup concept, corpus linguistics, text annotation. The inclusion criteria for an article in this review were the following: the study was of an exploratory or empirical nature or gives an idea of the terminology source.
A rich theoretical and empirical literature can be applied to the question on how to identify Value Proposition in startups texts. As the current research focuses on linguistic patterns of startups landing pages, the author reviews theoretical and empirical literature on specific online text features, textual value proposition methods, value proposition comprehension by consumers of different level of expertise.
Michael Lanning and Edward Michaels first used the expression “value
proposition” (VP) in a 1988 work document for the consulting company “McKinsey and Co”.
In the article, which was entitled “Delivering value to customers”, the authors define
value proposition as “a clear, simple statement of the benefits, both tangible and
intangible, that the company will provide, along with the approximate price it will
charge each customer segment for those benefits”[
In 2016, Eric Almquist suggested a strategy based on a differentiated customer
value proposition. A suggested set of value was called Elements of Value [
Fig. 2. Heuristic model of value with examples of companies exhibiting elements of value [
There are comparatively few researches on analyzing value propositions in online
startups. In 2007, Su-C Li in his paper argues that a properly constructed value
proposition is essential to the value creation process in e-business, and value co-production
is the building blocks for value protection mechanism in network economy [
Äyväri, Anne, and Annukka Jyrämä in their article “Rethinking value proposition
tools for living labs” published in 2017 provide a conceptual analysis on value
proposition tools to be used in future empirical research and in building managerial insight.
The conceptual analysis focuses on a living lab framework and recent theoretical
developments around the concept of value that are reflected in the context of three
managerial tools for creating value propositions. Among findings in the context of the
living labs approach, the Value Proposition Builder seems to conflict with the ideas
and premises of user-centric innovation processes [
Corpus linguistics has generated a number of research methods, which attempt to
trace a path from data to theory. Wallis and Nelson in 2001first introduced what they
called the 3A perspective - Annotation, Abstraction and Analysis: Annotation consists
of the application of a scheme to texts [
First Stage – Current A total of 800 different startups in the field of air transportation were chosen, their landing pages consist the research base for analysis on Value Proposition. 100 participants of low level expertise - IT, Aviation, and Management undergraduate students – were involved in a preliminary survey, post-survey, training and post-training annotation of startups landing pages. A separate webpage was created for conducting surveys, two-level training and annotation process. The quality of annotation as it was performed by low-expert students was assessed hereafter by industry experts.
The aim of a preliminary survey is to understand the level of expertise for nontrained participants. The students were told about the basic concept of value proposition. Also, they were given some examples of Value Proposition such as typical ones: affordability, quality, speed; less typical: eco-friendliness. After that they were asked to list as many values as they can, that are provided by the Air Transportation startups and companies. not be easily identified due to its vague expression or the creator of the webpage use non-standard ways to promote their startup, e.g. video or non-trivial words.
The participants have options to identify a landing page as not a startup, can state
that the value is hard to identify, and can name the page as not from the air
transportation industry. Also, they are asked to click a Like button if they think this startup
clearly deliver the VP.
Preliminary results. In the preliminary results the author observed that the current
assumption, based on the suggested algorithm, shows the expected ambiguity of the
natural language as well as the task complexity and ambiguity as perception of the VP
concept depends on the individual personal characteristics and background. Also, it
proves that the necessity of inter-annotator agreement is a measure of how well two
(or more) annotators can make the same annotation decision for a certain category
[
Future Work. The immediately future work is to identify the features (e.g. linguistic, semantic), so the author can determine specific for startups Value Proposition patterns to develop testable predictions. Creating transportation industry values ontology based on inter-annotated corpus with high ambiguity level is one of the objectives of this research. Building and testing a model is the next phase of the current studies. As the result of this work the author will elaborate a methodology for analysis of values based on a model of automatic identification of values in the text of a startup’s landing page in the air transportation industry.
At the end stage the author plans to develop a tool in the form of software for assisting industry analysts in their research and advisory services.
Factors that influence the performance of the Value Proposition identifying model are discovered during the annotation process. The author is analyzing participants’ comments and commits not only to identify the clearly viewed linguistic features, but also to find not so vivid patterns that we can use to automate the process of VP identification. There are several factors technical and non-technical that the author thinks can influence the performance of annotation process (like what influences the attitude towards and the time of annotation) and that are being investigated.
In this research the author empirically has studied the process of Value Proposition identification in the air transportation startups. The study has been conducted according to one of the well-recognized path of corpus linguistic research methods: 3A – Annotation, Abstraction, Analysis. The current stage of the given research is a landing text annotation process. An experiment design has been developed: preliminary survey, two-level training, dataset of objects, 100 annotators.
The preliminary findings show the importance of inter-annotator agreement and an ambiguity of natural language and value identification process. The next stage of this research will include identifying the linguistic features and patterns. It will be a basis of elaborating a methodology of automatic identification of values in the text of a startup’s landing page in the air transportation industry. At the end, the author plans to develop a tool in the form of software to assist industry analysts in their research and advisory services.