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Citation: Tanev, S.; Keen, C.; Bailetti,
T.; Hudson, D. Topic Modelling of
Management Research Assertions to
Develop Insights into the Role of
Artificial Intelligence in Enhancing the
Value Propositions of Early-Stage
Growth-Oriented Companies. Appl.
Sci. 2024,14, 3277. https://doi.org/
10.3390/app14083277
Academic Editors: Marco Palomino
and Craig McNeile
Received: 6 March 2024
Revised: 2 April 2024
Accepted: 8 April 2024
Published: 13 April 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
applied
sciences
Article
Topic Modelling of Management Research Assertions to Develop
Insights into the Role of Artificial Intelligence in Enhancing the
Value Propositions of Early-Stage Growth-Oriented Companies
Stoyan Tanev 1,* , Christian Keen 2, Tony Bailetti 1and David Hudson 1
1Technology Innovation Management Program, Sprott School of Business, Carleton University,
1125 Colonel By Dr., Ottawa, ON K1S 5B6, Canada; tonybailetti@cunet.carleton.ca (T.B.);
davidhudson@cunet.carleton.ca (D.H.)
2
Faculty of Business Administration, UniversitéLaval, 2325, Rue de la Terrasse, Québec, QC G1V 0A6, Canada;
christian.keen@fsa.ulaval.ca
*Correspondence: stoyan.tanev@carleton.ca
Abstract: The article suggests a Value Proposition (VP) framework that enables analysis of the
beneficial impact of Artificial Intelligence (AI) resources and capabilities on specific VP activities. To
develop such a framework, we examined existing business and management research publications to
identify and extract assertions that could be used as a source of actionable insights for early-stage
growth-oriented companies. The extracted assertions were assembled into a corpus of texts that was
subjected to topic modelling analysis—a machine learning approach to natural language processing
that is used to identify latent themes in large corpora of text documents. The topic modelling resulted
in the identification of seven topics. Each topic is defined by a set of most frequent words co-occurring
in a distinctive subset of texts that could be interpreted in terms of activities constituting the core
elements of the VP framework. We then examined each activity in terms of its potential to be enhanced
by employing AI resources and capabilities. The interpretation of the topic modelling results led to
the identification of seven topics: (1) Value created; (2) Stakeholder value propositions; (3) Foreign
market entry; (4) Customer base; (5) Continuous improvement; (6) Cross-border operations; and
(7) Company image. The uniqueness of the adopted topic modelling approach consists in the quality
of the assertions and the interpretation of the seven topics as an activity framework, i.e., in its capacity
to generate actionable insights for practitioners. The additional analysis suggests that there is a
potential for AI to enhance the emerging four core elements of the VP framework: Value created,
Stakeholder value propositions, Foreign market entry, and Customer base. More importantly, we
found that the greatest number of assertions related to activities that could be enhanced by AI are
part of the Customer base topic, i.e., the topic that is most directly related to the growth potential
of the companies. In addition, the VP framework suggests that a company’s customer base growth
is continuously enhanced through a positive loop enabled by activities focused on the Continuous
improvement of the activities and the amount of Value created, the alignment of Stakeholder value
propositions, and companies’ Foreign market entry. Thus, the multiple-stakeholder perspective on VP
development and foreign market entry appears as a factor that helps in understanding the beneficial
impact of AI on the enhancement of the VP of early-stage growth-oriented companies.
Keywords: topic modelling; natural language processing; value proposition enhancement; early-stage
growth-oriented company; artificial intelligence; business value; actionable insight
1. Introduction
Artificial intelligence (AI) is a constellation of many different technologies designed to
work together to enable machines to perform tasks with human-like levels of intelligence.
The adoption of AI technologies helps companies to reinvent the key elements of their
business strategies focusing on customers, competition, data, innovation, and value creation
Appl. Sci. 2024,14, 3277. https://doi.org/10.3390/app14083277 https://www.mdpi.com/journal/applsci
Appl. Sci. 2024,14, 3277 2 of 23
logic [
1
,
2
]. The need to synergize these elements is part of the challenges many businesses
face today. AI technologies have become a key business factor (some would even say
actor) that enables the kind of autonomous work processes and self-organizing systems
that can increase a firm’s economic rationality [
3
], market reach [
4
], and reduce barriers
to its international expansion [
5
,
6
]. AI technologies should therefore be seen as a key
component in the development of any firm’s digital strategy and one of the pillars of its
value proposition (VP) and business model innovation [
7
,
8
]. A firm’s meaningful adoption
of AI technologies increases the quality of its cross-border interactions with key business
stakeholders no matter what their geographic location or institutional distance are [
5
,
9
]. It
should be therefore considered from an ecosystem business perspective [5,10].
In this article, we examine how AI can enhance the VPs of early-stage companies
committed to business growth. These are not necessarily technology-based companies
that could somehow leverage the distinctive attributes of AI resources and capabilities as
part of their growth mechanisms. For such firms, leveraging AI resources and capabilities
would usually mean entering a dynamic three-party relationship involving its internal
users of AI as a principal, the AI agent or system, and the provider of the AI agent or
system [
3
]. Examples of the potential benefits of adopting AI resources, agents, and
systems by such companies include: transforming the efficiency of business processes that
require human intelligence; intensifying the economic rationality of the firm by enhancing
decision-making processes based on evidence-based, data-driven insights; enabling or
facilitating interactions between key stakeholders and other value creation actors that
might otherwise be inefficient or impossible; crossing the traditional boundaries of the
firm to enable seamless resource integration and communication across business units and
partners, fostering the emergence of a more agile and interconnected value creation and
delivery framework [3].
The early-stage companies we have in mind are not startups, but they are not fully
established or mature either. A key characteristic of such companies is their growth
orientation, which makes them open to refining and enhancing their VPs and value delivery
mechanisms in a way that could enable business growth. We focus on such companies for
two reasons. Firstly, the majority of early-stage firms are faced with the challenges of scaling
up. Though this challenge is not specific to unique countries or geographical regions, it is
particularly relevant to Canadian firms to which we are most directly exposed. For example,
a recent report by the Toronto Board of Trade states that “Canada is a terrific start-up nation
but a dismal failure as a scale-up nation” [
11
]. Secondly, another key challenge for such
companies is the need to develop capabilities to access, combine, and deploy resources
provided by external resource owners (including AI resources, agents, and systems) that
they could not develop on their own [
12
–
14
]. Thus, examining such firms necessarily
requires a multi-stakeholder perspective on VP development since they must design and
align a VP portfolio to engage with all relevant stakeholders, including investors, suppliers,
distributors, and partners, rather than trying to address the needs of customers alone [
12
].
Our focus on the VP construct is not accidental. A VP is the best expression of a
company’s business strategy and innovative capacity, that is, its ability to coordinate a
combination of resources from multiple stakeholders to develop new products and services
and shape valuable market offers to address the needs of specific customer target groups
and compete in the marketplace [
15
,
16
]. Our study considers the VP as an integrative
construct and positions VP design, development, and delivery as a bridge between the
formulation of a business strategy and the development and implementation of a corre-
sponding viable business model. Our research focus aligns with recent VP research studies
which emphasize that a VP is “a strategic tool that is used by a company to communicate
how it aims to provide value to customers” ([
16
], p. 467) and a concept that was initially
“meant to help re-focus how managers think about business strategy” ([
17
], p. 307). The
unique strategic role of VPs in engaging customers and other relevant business stakeholders
has been widely acknowledged in the literature (e.g., [12,18–20]).
Appl. Sci. 2024,14, 3277 3 of 23
Our research study stems from a belief that research on early-stage growth-oriented
companies is a fruitful arena where AI-based digitalization and VP research streams could
be merged to help develop valuable insights for both scholars and practitioners [
12
]. The
integration of the two streams helps to address an existing research gap associated with
the lack of actionable VP frameworks that incorporate a multiple-stakeholder perspective,
a focus on growth orientation in the context of early-stage companies, and the role of AI
resources and capabilities in enhancing the VPs of such companies. The logic behind this
integrative approach could be summarized as follows: (i) to grow, early-stage companies
need resources and capabilities provided by multiple external stakeholders; (ii) the need
to engage multiple external stakeholders requires a multiple-stakeholder perspective on
the reshaping and enhancement of their VPs in line with their strategic growth objectives;
(iii) AI agents and systems are among the most valuable resources that could be provided
by external stakeholders to early-stage companies interested in growing by enhancing their
VPs; (iv) there is a lack of actionable frameworks that could help in using AI resources and
capabilities to enhance the VPs of growth-oriented early stage companies. There is a need,
therefore, for the development of such an actionable VP enhancement network to bridge
the gap between management research and business practice. Indeed, Berglund et al. [21]
highlighted the need for a distinct body of pragmatically oriented knowledge that could
bridge the gap between entrepreneurship theory and entrepreneurial practice. The research
problematics addressed in this article is an explicit attempt to address this need.
Therefore, this article aims to explore how AI can help the enhancement of the VPs
of early-stage companies. To do so, we sought to develop a VP framework providing an
explicit business activity structure that could enable an analysis of the potential beneficial
impacts of AI resources and capabilities. To develop such a framework, we examined
the extant literature to generate a corpus of assertions articulating actionable insights that
could inform the enhancement of firms’ VPs. We then performed topic modelling [
22
–
26
]
to identify an emerging set of groups of activities that could constitute the core elements of
a VP enhancement framework. We then examined each activity in terms of its potential
to be enhanced by AI resources and capabilities and identified the ones that could. Our
analysis then sought conclusions that could be used as future research propositions for
scholars or as actionable insights for practitioners as advised by Makadok et al. [27].
The study we led contributes to the literature in two different ways. The first is
methodological, since this is the first time that topic modelling has been applied to a
corpus of management research assertions to develop an activity-based VP enhancement
framework. Topic modelling is the process of identifying latent topics in a large set of text
documents. It is an example of a Natural Language Processing (NLP) method that examines
large collections of unstructured text data to identify topics otherwise impossible to find
through human efforts alone. The various applications of topic modelling in management
research have recently been summarized by Hannigan et al. [
24
]. Its application in the
study discussed here is an example of how an NLP technique could benefit VP research
in the context of early-stage growth-oriented companies. The study’s second contribution
lies in developing insights into how the adoption of AI resources and capabilities can
enhance the business activities identified as core elements of the proposed VP enhance-
ment framework. As such, our results ultimately seek to make explicit the link between
companies’ AI resources and capabilities and the enhancement of their VPs, and inspire
business practitioners to examine the applicability of the analytical framework, as well
as motivate other scholars to apply the framework in future empirical studies involving
real-life company cases.
2. Literature Review
2.1. The Business Value of AI Resources and Capabilities
Recent research on the business value of AI suggests that AI resources and capabilities
could offer firms a significant value-driving impact and help them achieve an operational
and competitive advantage, even if there is a significant lack of understanding about how
Appl. Sci. 2024,14, 3277 4 of 23
to appropriate value from AI [
28
]. An increasing number of studies focus on examining the
specific dimensions of value that could be enabled through AI resources and capabilities.
For example, Wagner ([
3
], p. 3) defines a firm as artificially intelligent if it “deploys
classic economic factors of production human labor, capital and land in combination with
machine labor in the form of AI agents.” He adopts the economic theory of the firm to
systematically explore five ways AI might impact it, specifically: AI intensifies the effects
of economic rationality on the firm; AI introduces a new type of information asymmetry;
AI can perforate the boundaries of the firm; AI can create triangular agency relationships;
and, AI has the potential to remove the traditional limitations of integration.
Davenport and Ronanki [
29
] emphasize how businesses should examine the poten-
tial value of AI through the lens of business rather than technological capabilities. They
point out that AI can support the automation of business processes, gain competitive
insight through data analysis, and engage with customers and employees. Majhi et al. [
30
]
consider AI and machine learning as subfields of cognitive computing and cognitive tech-
nologies. They developed a conceptual model that shows how cognitive analytics (CA)
technologies can add value to organizations by enabling and enhancing three dynamic
organizational capabilities: sensing, seizing, and reconfiguring. CA technologies enhance a
firm’s sensing capabilities by helping them to collect and analyze real-time data, anticipate
and explore new trends, and gather information from multiple sources to capture emerging
user behaviour across different markets and contexts. In addition, CA helps firms seize
opportunities by facilitating agile development and allowing production and resource
utilization process adjustments. It facilitates the shaping of new strategies and innovative
offerings by substantially increasing the speed and efficiency of decision-making. Finally,
CA enhances the capacity of firms to reconfigure which, according to Teece ([31], p. 1319),
involves “enhancing, combining, protecting and, when necessary, reconfiguring the busi-
ness enterprise’s intangible and tangible assets” to avoid inertia and path dependencies.
CA technologies empower a firm’s dynamic reconfiguring capabilities and enable them
to reshape markets [
32
] and expand the frontiers of analytics-based decision-making by
shaping new valuable partnerships, mergers, and acquisitions [33].
Huang and Rust [
34
] developed a strategic AI-usage framework to help firms engage
with customers and offer them different service-based benefits. This framework sees AI
expanding across three fronts: mechanical, thinking, and feeling. Mechanical AI could
help in terms of cost leadership and standardization, primarily at the service delivery
stage and when service is routine and transactional. Thinking AI could help in terms of
quality leadership and personalization, primarily at the service creation stage and when
service is data-rich and utilitarian. Feeling AI could help in terms of relationship leadership,
primarily at the service interaction stage and when service is relational and high contact
([34], p. 36).
Paschen, Wilson and Ferreira [
35
] offer a comprehensive discussion of the role of AI
in enhancing sales processes. They identified the value of AI systems at each stage of the
sales funnel, and also clarified the role of human intelligence and decision-making at each
stage of this AI-enabled sales funnel. They posit that there is a complementarity between
humans and AI, and that AI’s enormous information processing capacity can augment
human intelligence or even replace well-defined and repeatable human tasks in a B2B
sales context. For example, it could help build rich customer prospect profiles, update lead
generation and lead qualification models via machine learning, personalize and customize
communication messages and channels, establish contacts via digital agents (e.g., chatbots),
enable fast prototyping, curate competitive intelligence, enable dynamic pricing, automate
workflows and post-order services, and uncover new customer needs, just to name a few.
Güngör [
36
] explains that organizations can explore two major AI value-creation
opportunity pathways: one lying in the value chain, and one emerging from the adoption of
a multi-stakeholder benefit analysis perspective. For example, AI could be fully integrated
into business value chains and, more specifically, use replenishment models to manage
inbound logistics, robots in operations and order fulfillment, dispatch algorithms for
Appl. Sci. 2024,14, 3277 5 of 23
delivery cost optimization, and recommendation engines to optimize service levels. Güngör
argues that AI could also provide support functions in human resources “to predict the
best candidates to hire, in finance to prevent frauds, in procurement to optimize number of
suppliers, etc.” ([
36
], p. 75). The second opportunity pathway relates to multi-stakeholder
benefit analysis of revenue growth, cost savings, risk mitigation or customer experience,
and more. Such analyses should help evaluate how value is shared or distributed and
if there exists any potential conflict of interest between stakeholders (be they customers,
employees, suppliers, co-innovation partners, or society at large).
AI researchers and practitioners have worked together to instrumentalize the integra-
tion of AI resources and capabilities into business development processes. One example
of these joint efforts is the development of canvas approaches in the implementation of
AI business value [
37
,
38
]. The adoption of AI canvas approaches would indicate that the
AI field is moving toward a higher stage of maturity which should inspire even more
researchers to invest themselves in this domain.
2.2. Value Propositions in the Context of New Growth-Oriented Companies
The importance of the VP construct, and the multiple issues associated with the de-
velopment of VPs, have been discussed in the literature [
39
–
41
]. Despite this, the VP
construct has often been used casually and applied haphazardly rather than strategically
and rigorously [
42
]. According to Webster [
43
], a VP should be the company’s single
most important organizing principle and thus one of the company’s most valuable re-
sources [
12
]. VPs should therefore have a strong influence on key aspects of any business,
such as the acquisition of complementary resources needed for the value creation process,
operations management, inter-organizational structures, and interactions with all relevant
stakeholders. The design and formulation of VPs should therefore be done using a multi-
stakeholder perspective to reciprocally align all VPs for all relevant actors in the business
ecosystem [12,44,45].
According to Bailetti et al. [
12
] and Nambisan et al. [
5
], the extant literature appears to
overlook the important link between a company’s VP portfolio and its business strategy.
Onetti et al. [
19
] make a clear distinction between a firm’s business model and its strategic
concepts and claim that business model frameworks should exclude the VP construct
which, according to them, should be part of the higher order of a firm’s strategic elements
(Onetti et al. [
19
] refer to Kothandaraman and Wilson [
46
], and Winter [
20
]). Amit and
Zott [
18
] also point out that early definitions of the VP construct emphasize links to a
firm’s strategy and performance by observing that a winning strategy is always rooted in a
superior VP (Amit and Zott [
18
] refer to Lanning and Michaels [
47
]). Amit and Zott [
18
],
Bailetti et al. [
12
], and Nambisan et al. [
5
] agree that VPs should be examined from a
strategic point of view since every focal firm needs to offer some form of VP not only to
customers but also to all other stakeholders involved in its business model. In a recent
article, Michael Lanning [
17
], who is the inventor of the term VP, has explicitly emphasized
the strategic aspect of VPs. According to Lanning, “the strategic point of a VP should be
to deliver it: choose, then both provide it (actually make it happen in the experiences of
customers) and, of course, communicate it. Thus, a business should be understood and
managed as a ‘value delivery system’” ([
17
], p. 306), Lanning’s italic. For him, the context
for VPs is the concept of a value delivery system. This concept “was meant to help re-focus
how managers think about business strategy (a suggestion which I still think appropriate
though not fully appreciated today). Rather than deciding what product (or service) a
business should invent, produce and market (and how), strategy should instead design
what VP to provide and communicate to customers (and how)” ([17], p. 307).
Several studies have highlighted the importance given by marketing scholars to cus-
tomer VPs [
41
,
48
]. Payne et al. ([
16
], p. 472) define a customer VP as “a strategic tool
facilitating communication of an organization’s ability to share resources and offer a su-
perior value package to targeted customers”. The marketing literature has emphasized
the need for firms to articulate benefits and costs relevant to targeted customers as well as
Appl. Sci. 2024,14, 3277 6 of 23
functional and experiential features of the differentiation aspects of their offerings and of
customer experience in particular [
16
,
49
]. The customer is indeed a key stakeholder, but
not the only stakeholder, and, for many ventures, not always the most important one. The
entrepreneurship and innovation literature shows that ventures tend to focus on the devel-
opment of new products and services when targeted markets are not clearly defined [
50
,
51
].
Recent literature has also emphasized the importance of proper resource configuration and
practices in enabling the delivery of key benefits, the sharing of resources, and the inte-
gration of the value chain’s key actors in a value co-creation process [
52
].
Skålén et al. [53]
define VPs as “promises of value creation that build upon the configuration of resources
and practices” (p. 144). They emphasize the importance of value co-creation with customers
as well as the integration of resources provided by other relevant stakeholders.
Little is known about the factors that enable early-stage growth-oriented companies to
scale rapidly and the ways they can align their VP portfolio with their scaling objectives.
Such an alignment implies the need to incorporate any scale-up objectives into a company’s
business model through its configuration of the resources and activities that not only
create value for customers but also allow it to capture part of that value and distribute it
back to key resource owners [
54
]. Scalability is defined as the extent to which a VP and
its corresponding business model can help achieve the desired value creation and value
capture targets by increasing the customer base without having to proportionately add
additional resources [
55
]. As suggested by Shepherd et al. [
56
], the organizing phase of a
venture has a profound impact on its performing phase that in turn will (or will not) lead
to any potential scale-up.
Recent studies have identified an explicit link between the growth orientation of new
technology companies and the novelty and attractiveness of their VPs. Rydehell et al. [
57
]
point out that finding new and innovative ways to offer value to customers is important for
any company to achieve high sales growth, as well as rapid geographic expansion to new
markets. Malnight et al. [
58
] suggest that companies pursue high growth by creating new
markets, serving broader stakeholder needs, changing the rules of the game, redefining
the playing field, and reshaping their VPs. Bailetti et al. [
12
] point out that such companies
should develop capabilities to access, combine, and deploy the resources required to
create value and scale by offering their external resource owners returns they could not
otherwise obtain on their own [
13
,
14
]. According to Bailetti et al. [
12
], the VPs of new
growth-oriented companies have two distinctive features: they enable themselves and any
number of external stakeholders to directly transact without an intermediary, and they
increase investments that help create and improve business transactions over time.
Bailetti et al. [
12
] also identified three factors that make the VP portfolio one of the
most valuable company resources: it strengthens the company’s scale-up capabilities,
it increases the demand for its products and services, and it fosters investments in the
conceptualization, development, maintenance, and refinement of its VPs [12].
2.3. Ecosystem Perspective on Value Propositions
Any new venture requires people with entrepreneurial imaginativeness [
56
]. The
act of conceptualizing a new venture is “a cognitive skill that combines the ability of
imagination with the knowledge needed to stimulate various task-related scenarios in
entrepreneurship” ([
59
], p. 2266). This cognitive skill is essential to take an idea and move
toward identifying an opportunity and then creating a venture to exploit it [
56
]. It implies
the shaping of a novel offering [
60
] through the exploitation of resources beyond the ones
controlled by the nascent company [
61
,
62
]. Such exploitation results from a multi-actor
process in which different business ecosystem stakeholders are actively engaged [
63
] (Stam
and van de Ven 2019).
In a globalized world with increasingly complex and interrelated technologies, en-
trepreneurial innovation ecosystems are vital for ventures looking to implement complex
VPs (e.g., [
63
,
64
]). Every venture committed to scale operates in an environment where dif-
ferent ecosystem actors are actively engaged in complementing the value creation process.
Appl. Sci. 2024,14, 3277 7 of 23
Any such venture needs to join a business ecosystem that provides it with the “alignment
structure of the multilateral set of partners that need to interact in order for a focal value
proposition to materialize” ([
63
], p. 42). Securing the commitment of valuable stakehold-
ers can be crucial to a venture’s scaling performance [
56
,
65
]. An ecosystem defines and
provides processes and rules that help resolve any emerging coordination issues and also
encourages alignment between ecosystem actors through rules of engagement, standards,
and codified interfaces [
66
]. The modularity of resources and contributions is necessary,
but in and of itself an insufficient condition for the existence of an ecosystem (Baldwin 2008;
Langlois 2003). For an ecosystem to emerge and be useful, there must be a significant need
for coordination that cannot be met by the hierarchy imposed by a focal firm [
64
]. What
makes ecosystems unique is that actors “can choose among the components (or elements of
offering) that are supplied by each participant, and can also, in some cases, choose how
they are combined” ([66], p. 2260).
Jacobides et al. [
66
] identified two types of complementarities that can unambiguously
characterize the coordination of activities and resource sharing between ecosystem actors.
The first is unique complementarity, either where an activity or component offered by
one actor requires the activity or component of another, but not vice versa, or where
two activities, A and B, of two different actors both require each other. The second,
supermodular complementarity, could be described as “more of A makes B more valuable,
where A and B are two different products, assets, or activities” (p. 2262). The distinctive
feature of ecosystems is that they provide an alignment structure where different actors
can engage with each other in value creation through these unique and supermodular
complementarities, in production and/or consumption, which can both be coordinated
without the need for vertical integration. Defining business ecosystems in this way offers
an opportunity to advance VP research and practice.
3. Research Methodology
The objective of our research study is to explore how AI can help the enhancement of
the VPs of early-stage growth-oriented companies. We address the lack of research in this
domain by developing an actionable VP framework providing an explicit business activity
structure that could enable the analysis of the potential beneficial impact of AI resources
and capabilities.
In earlier publications, we articulated some preliminary insights referring to VP
enhancement in the context of new growth-oriented companies [
12
,
67
,
68
]. In the present
article, we summarize the outcomes of the research process we used (see Figure 1) to
build on our preliminary insights by: (a) selecting a list of relevant research articles in
business and management; (b) examining each article to extract paragraphs suggesting or
implying business activities that could enable the enhancement of the VPs of early-stage
growth-oriented companies; (c) transforming the paragraphs into actionable assertions and
building a corpus of such assertions that could be used as a basis for text analytics; (d) using
topic modelling to structure the corpus of research assertions in a set of emerging themes
to be interpreted as distinctive groups of activities that could help to enhance the VPs
of early-stage growth-oriented companies; (e) identifying the potential relation between
the different ways of enhancing the VPs by examining the textual (semantic) proximity
between the assertions included in each of the groups of activities identified in the topic
modelling process; (f) examining which of the distinctive groups of activities could be
enhanced by the adoption of AI resources or capabilities; and (g) drawing conclusions
based on the results.
We searched the Web of Science Core Collection database for journal research articles
containing the string “value proposition” in the titles, then selected the subset of articles
corresponding to the Business or Management Web of Science categories, then searched
everywhere within the last subset of articles for “business model”, which narrowed down
our search to a list of 84 articles. The use of the additional “business model” search term was
justified by the assumption that it will help in selecting articles including more operational
Appl. Sci. 2024,14, 3277 8 of 23
or actionable insights. This initial list of 84 articles was further examined by all co-authors to
gauge the potential relevance of the articles to the objective of our study by providing more
comprehensive analysis, descriptions of VP frameworks, managerial recommendations, and
actionable insights of practical relevance for real-world companies. A subset of 14 articles
was selected as the core source of research assertions [
12
,
16
,
32
,
41
,
44
,
45
,
48
,
53
,
57
,
58
,
69
–
72
].
These articles were then examined independently by all co-authors and used as a source for
the formulation of distinguishable actionable insights. The VP insights were complemented
by insights extracted from some of the most representative references provided in the above
14 articles as well as from recent research articles focused on business ecosystems [
63
,
64
,
66
]
and AI-based business value [28,30,35,36].
Appl. Sci. 2024, 14, x FOR PEER REVIEW 8 of 23
enhanced by the adoption of AI resources or capabilities; and (g) drawing conclusions
based on the results.
Figure 1. Research steps used to develop a value proposition enhancement framework.
We searched the Web of Science Core Collection database for journal research articles
containing the string “value proposition” in the titles, then selected the subset of articles
corresponding to the Business or Management Web of Science categories, then searched
everywhere within the last subset of articles for “business model”, which narrowed down
our search to a list of 84 articles. The use of the additional “business model” search term
was justified by the assumption that it will help in selecting articles including more oper-
ational or actionable insights. This initial list of 84 articles was further examined by all co-
authors to gauge the potential relevance of the articles to the objective of our study by
providing more comprehensive analysis, descriptions of VP frameworks, managerial rec-
ommendations, and actionable insights of practical relevance for real-world companies. A
subset of 14 articles was selected as the core source of research assertions
[12,16,32,41,44,45,48,53,57,58,69–72]. These articles were then examined independently by
all co-authors and used as a source for the formulation of distinguishable actionable in-
sights. The VP insights were complemented by insights extracted from some of the most
representative references provided in the above 14 articles as well as from recent research
articles focused on business ecosystems [63,64,66] and AI-based business value
[28,30,35,36].
The process of turning research paragraphs into assertions (see examples provided
in Table 1) included multiple informal discussions with representatives of our local entre-
preneurial and innovation ecosystem such as entrepreneurs, business mentors, represent-
atives of organizations supporting small and medium company innovation, and other re-
searchers, who had experience working with early-stage companies. This feedback helped
the final formulation of the assertions and ensured the use of language that is familiar to
executive managers of such companies. The process resulted in a corpus of 182 assertions
Figure 1. Research steps used to develop a value proposition enhancement framework.
The process of turning research paragraphs into assertions (see examples provided
in Table 1) included multiple informal discussions with representatives of our local en-
trepreneurial and innovation ecosystem such as entrepreneurs, business mentors, rep-
resentatives of organizations supporting small and medium company innovation, and
other researchers, who had experience working with early-stage companies. This feedback
helped the final formulation of the assertions and ensured the use of language that is
familiar to executive managers of such companies. The process resulted in a corpus of
182 assertions referring to the shaping, enhancement and alignment of VPs (most of these
assertions are shown in Appendix Aunder the specific topics that were identified in the
topic modelling results section).
The next step of our study involved topic modelling analysis [
12
,
23
–
26
] as a text
mining approach to the identification of emerging latent themes that could be associ-
ated with the groups of activities defined by the texts included in the corpus of 182 as-
sertions. In our study, we have used the Topic Model Explorer (https://github.com/
michaelweiss/topic-model-explorer (accessed 6 April 2024)) and the text analytics ca-
pabilities of the Orange Data Mining tool, (Orange Data Mining open source software
Appl. Sci. 2024,14, 3277 9 of 23
tool: https://orangedatamining.com/ (accessed on 6 April 2024)) which incorporates
one of the most popular topic modelling algorithms, known as Latent Dirichlet Alloca-
tion, or LDA [
22
]. Details about topic modelling with the Orange tool can be found here:
https://orangedatamining.com/widget-catalog/text-mining/topicmodelling-widget/ (ac-
cessed on 6 April 2024)). The LDA method has gained popularity due to its ability to
process large corpora of text documents, resulting in the identification of emerging latent
topics across all the documents (it is now a built-in option in many commercial and open
source text analytics tools). It posits that each document is a mixture of a small number
of topics and that each word in a document could be associated with one or more of the
document’s topics. In more technical terms, the LDA approach inputs a document-term
matrix, for which the rows are the specific word counts and the columns are the specific
texts corresponding to the documents in the corpus.
Table 1. Three examples of transforming research paragraphs into actionable assertions.
Research Assertion Reference Original Paragraph in the Reference
Align value propositions for all relevant
stakeholders by developing an objective
that benefits them all.
Ballantyne et al.
[44], p. 202
“The concept of reciprocal value propositions is examined
in the light of S-D logic’s fundamental premises.
Examples are included to show how reciprocal value
propositions can be used to initiate and guide resource
integration activities between initiators and participants
across a range of stakeholders of the firm”.
Be responsible and accountable for
creating and making visible the
quantifiable benefits the company
delivers to its stakeholders.
Payne et al.
[41], p. 250
“Research in B2B markets suggests that most firms have
difficulty developing VPs that demonstrate quantifiable
benefits to their customer base.
. . .
Addressing this issue is
of considerable importance”.
Combine two or more resources to create
value that exceeds the sum of the value
created from each resource separately.
Bailetti et al.
[12], p. 21
“Deploy combinations of resources that will create value
that exceeds the sum of the value created from each
resource separately”.
LDA identifies distinct topics across the corpus by observing words that tend to co-
occur frequently within each of the texts. It outputs a document-topic matrix, for which
each document is assigned to a probabilistic mixture of topics. The combinations of words
per topic help identify specific themes that are latently present in the corpus. LDA also
organizes the corpus by clustering the assertions corresponding to each topic (as shown
in Figure 2). The assertions clustered in a given topic are ranked in terms of the degree of
their association with it. A closer examination of the topical organization of the assertions
enables the interpretation of the overall theme and the labelling of the topics [
73
]. The
number of topics to be used in the analysis is specified by the researchers. Figure 2provides
a symbolic representation of the topic modelling process. A more detailed description of
the original algorithm can be found in Blei [22].
Figure 3provides a process view of the topic modelling analysis in the way it was
performed in the Orange Data Mining tool. The key steps in the process include the loading
of the corpus of 182 assertions, pre-processing of the text (lowercase the text, split it into
words with tokenization, lemmatize tokens to their base form and finally remove stopwords,
i.e., common frequent words that appear in multiple topics and do not contribute to the
distinctiveness of the topic), topic modelling using the built-in LDA algorithm, and post-
processing and saving the results for further analysis, which includes the interpretation of
the topics such as is shown in Figure 2.
Our next step was to examine the consistency of the topics as a whole and the possibil-
ity of considering the groups of VP enhancement activities associated with each of them
as key elements of an analytical VP framework suitable for early-stage growth-oriented
companies. The last step, and the ultimate goal of our study, was to examine the extent to
which the activities suggested by the assertions and associated with a given topic could
be enhanced by means of AI resources and capabilities. Following this examination, we
Appl. Sci. 2024,14, 3277 10 of 23
reflected on the ability of AI and AI-driven digitalization to enhance the VPs of early-stage
growth-oriented companies.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 10 of 23
Figure 2. Symbolic representation of the logic and outcomes of the LDA topic modelling algorithm.
This is just an example with four topics, which is not the case for the results presented in this article.
Figure 3 provides a process view of the topic modelling analysis in the way it was
performed in the Orange Data Mining tool. The key steps in the process include the load-
ing of the corpus of 182 assertions, pre-processing of the text (lowercase the text, split it
into words with tokenization, lemmatize tokens to their base form and finally remove
stopwords, i.e., common frequent words that appear in multiple topics and do not con-
tribute to the distinctiveness of the topic), topic modelling using the built-in LDA algo-
rithm, and post-processing and saving the results for further analysis, which includes the
interpretation of the topics such as is shown in Figure 2.
Figure 3. Schematics of the workflow of the Orange Data Mining text analytics tool used to perform
the topic modelling analysis.
Our next step was to examine the consistency of the topics as a whole and the possi-
bility of considering the groups of VP enhancement activities associated with each of them
as key elements of an analytical VP framework suitable for early-stage growth-oriented
companies. The last step, and the ultimate goal of our study, was to examine the extent to
which the activities suggested by the assertions and associated with a given topic could
be enhanced by means of AI resources and capabilities. Following this examination, we
reflected on the ability of AI and AI-driven digitalization to enhance the VPs of early-stage
growth-oriented companies.
Figure 2. Symbolic representation of the logic and outcomes of the LDA topic modelling algorithm.
This is just an example with four topics, which is not the case for the results presented in this article.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 10 of 23
Figure 2. Symbolic representation of the logic and outcomes of the LDA topic modelling algorithm.
This is just an example with four topics, which is not the case for the results presented in this article.
Figure 3 provides a process view of the topic modelling analysis in the way it was
performed in the Orange Data Mining tool. The key steps in the process include the load-
ing of the corpus of 182 assertions, pre-processing of the text (lowercase the text, split it
into words with tokenization, lemmatize tokens to their base form and finally remove
stopwords, i.e., common frequent words that appear in multiple topics and do not con-
tribute to the distinctiveness of the topic), topic modelling using the built-in LDA algo-
rithm, and post-processing and saving the results for further analysis, which includes the
interpretation of the topics such as is shown in Figure 2.
Figure 3. Schematics of the workflow of the Orange Data Mining text analytics tool used to perform
the topic modelling analysis.
Our next step was to examine the consistency of the topics as a whole and the possi-
bility of considering the groups of VP enhancement activities associated with each of them
as key elements of an analytical VP framework suitable for early-stage growth-oriented
companies. The last step, and the ultimate goal of our study, was to examine the extent to
which the activities suggested by the assertions and associated with a given topic could
be enhanced by means of AI resources and capabilities. Following this examination, we
reflected on the ability of AI and AI-driven digitalization to enhance the VPs of early-stage
growth-oriented companies.
Figure 3. Schematics of the workflow of the Orange Data Mining text analytics tool used to perform
the topic modelling analysis.
4. Topic Modelling Results
4.1. Topic Model of the Corpus of Actionable Value Proposition Insights
Our topic modelling analysis identified seven topics, each defined by a set of words
and a set of topic-specific assertions. The words and the assertions associated with each of
the seven topics are listed in Appendix A. A close examination of these sets of assertions
allowed us to label each of the sets as follows: Value created,Stakeholder value propositions,
Foreign market entry,Customer base,Continuous improvement,Cross-border operations, and
Company image. The purpose of the selection of these specific labels was to emphasize
the thematic distinctiveness of each topic and shed light on the overall content of their
corresponding assertions. The labels should therefore be seen as thematic pointers emerging
from the topics and not as comprehensive content signifiers. The specific assertions are the
real content of each topic.
Topic 1 (Value created) refers to a given venture’s access to, and combination of, com-
plementary internal and external resources relevant to value creation for all its relevant
stakeholders. Topic 2 (Stakeholder value propositions) focuses on various aspects and the
alignment of the VPs to key stakeholders such as investors, customers, suppliers, etc.
Topic 3 (Foreign market entry) includes assertions related to successfully turning local offers
Appl. Sci. 2024,14, 3277 11 of 23
into global ones. Topic 4 (Customer base) focuses on activities that result in engaging and
attracting more customers, i.e., activities associated with the ultimate goal of a scaling
strategy, that is, increasing the customer base profitably. Topic 5 (Continuous improvement)
includes assertions about improving the value creation process and refining the alignment
of the company’s VP portfolio. Interestingly, we find here specific assertions about improv-
ing the firm’s cybersecurity practices, which refer to online business interactions and global
reach. Topic 6 (Cross-border operations) focuses on knowledge sharing and cross-border coor-
dination activities as well as on the need for stronger positioning in cross-border business
networks. Topic 7 (Company image) refers to the brand identity of the company and how it
differentiates itself from its competitors.
4.2. Shaping a Value Proposition Enhancement Framework
The seven topics outlined above do already suggest a conceptual framework sub-
stantiated by the specificity of the groups of assertions associated with each topic. To
examine the potential logical relations between them, the assertions associated with each
topic were conglomerated into seven documents, one per topic, and the cosine similarity
values between the seven documents were calculated. Cosine similarity is a measure used
to evaluate the semantic proximity of documents or provide a ranking of documents with
respect to a given set of search words, a search phrase, or a paragraph [74].
The resulting VP framework is shown in Figure 4. The numbers along the links
between topics indicate the normalized cosine similarity between the seven documents
created by the process. The cosine similarity values were normalized by the maximum value
that was found between Topic 1 (Value created) and Topic 2 (Stakeholder value propositions).
The cosine similarity values shown here are presented on a 1 to 10 scale, including only the
values/links that are equal to or greater than 2.
Appl. Sci. 2024, 14, x FOR PEER REVIEW 12 of 23
Figure 4. VP enhancement framework showing the textual proximity (or semantic relations) be-
tween the seven emerging topics. Each topic is interpreted as a VP element including a set of action-
able insights for early-stage companies for which Customer base appears as a natural growth-related
metric.
It is important to note that the relationships between different topics should not be
considered in absolute terms. Rather, they should be seen as a way of using the semantic
proximity of the topic-specific assertions to form the basis for shaping the VP enhance-
ment framework. It should be also noted that this is not just a conceptual framework, this
is actually a structured business activity system which provides actionable insights for
practitioners. The seven elements of this VP framework and the resulting semantic links
between them allowed us to develop multiple insights, which we outline next.
5. Analysis of the Results
5.1. Three VP Elements Related to Customer Base Growth
A straightforward interpretation of the VP enhancement framework shown in Figure
4 suggests that there is a close interrelation between four core VP elements (shown in red
boxes in Figure 4): Stakeholder value propositions, Foreign market entry, Value created and Cus-
tomer base. Our claim about a closer interrelationship between these four core elements is
based on the fact that each of these elements is related to the other three. Customer base
appears as a natural dependent variable associated with the context of early-stage growth-
oriented companies. Our framework suggests that a company’s customer base is related
to the efficiency of the company’s value creation processes (i.e., its ability to access, com-
bine and align resources provided by the key actors engaged in their business ecosystem;
see assertions associated with Value created [Topic 1] in Appendix A), its foreign market
entry strategy (i.e., its strategic plan to penetrate global market locations by transforming
its local offers into global ones; see assertions associated with Foreign market entry [Topic
3] in Appendix A); and the aractiveness and alignment of its VPs in relation to key mem-
bers of their business ecosystem (i.e., its ability to shape a VP portfolio in alignment with
its scaling objectives; see assertions associated with Stakeholder value propositions [Topic 2]
in Appendix A). From a logical point of view, the link between Topic 1 (Va l u e c reated) and
Topic 2 (Stakeholder value propositions) could be considered as a core value creation axis
that could be further enhanced by Foreign market entry (Topic 3) and Cross-border business
activities (Topic 6) to enable Customer base growth (Topic 4).
These findings are in line with some of the key insights highlighted in our literature
review—the multiple-stakeholder perspective on VPs and business growth. The main
value of our suggested framework (Figure 4) is in: (i) the specificity of the activities it
Figure 4. VP enhancement framework showing the textual proximity (or semantic relations) between
the seven emerging topics. Each topic is interpreted as a VP element including a set of actionable
insights for early-stage companies for which Customer base appears as a natural growth-related metric.
It is important to note that the relationships between different topics should not be
considered in absolute terms. Rather, they should be seen as a way of using the semantic
proximity of the topic-specific assertions to form the basis for shaping the VP enhancement
framework. It should be also noted that this is not just a conceptual framework, this
is actually a structured business activity system which provides actionable insights for
practitioners. The seven elements of this VP framework and the resulting semantic links
between them allowed us to develop multiple insights, which we outline next.
Appl. Sci. 2024,14, 3277 12 of 23
5. Analysis of the Results
5.1. Three VP Elements Related to Customer Base Growth
A straightforward interpretation of the VP enhancement framework shown in Figure 4
suggests that there is a close interrelation between four core VP elements (shown in red
boxes in Figure 4): Stakeholder value propositions,Foreign market entry,Value created and
Customer base. Our claim about a closer interrelationship between these four core elements
is based on the fact that each of these elements is related to the other three. Customer base
appears as a natural dependent variable associated with the context of early-stage growth-
oriented companies. Our framework suggests that a company’s customer base is related to
the efficiency of the company’s value creation processes (i.e., its ability to access, combine
and align resources provided by the key actors engaged in their business ecosystem; see
assertions associated with Value created [Topic 1] in Appendix A), its foreign market entry
strategy (i.e., its strategic plan to penetrate global market locations by transforming its
local offers into global ones; see assertions associated with Foreign market entry [Topic 3] in
Appendix A); and the attractiveness and alignment of its VPs in relation to key members
of their business ecosystem (i.e., its ability to shape a VP portfolio in alignment with its
scaling objectives; see assertions associated with Stakeholder value propositions [Topic 2] in
Appendix A). From a logical point of view, the link between Topic 1 (Value created) and
Topic 2 (Stakeholder value propositions) could be considered as a core value creation axis
that could be further enhanced by Foreign market entry (Topic 3) and Cross-border business
activities (Topic 6) to enable Customer base growth (Topic 4).
These findings are in line with some of the key insights highlighted in our literature
review—the multiple-stakeholder perspective on VPs and business growth. The main value
of our suggested framework (Figure 4) is in: (i) the specificity of the activities it incorporates;
(ii) the way it structures the actionable insights around specific topics (VP elements); and
(iii) its explicit identification of the logical relation between the four core VP elements in
the context of early-stage growth-oriented companies.
5.2. VP Enhancement Is a Continuous Process
Our VP framework suggests that a company’s Customer base growth is continuously
enhanced through a positive loop enabled by activities focused on the Continuous im-
provement of the Value created, the alignment of Stakeholder value propositions, and Foreign
market entry processes (see assertions associated with Continuous improvement [Topic 5] in
Appendix A
) and on the Company image (see assertions associated with this topic [Topic
7] in Appendix A). This finding helps emphasize the dynamic nature of VP enhancement
activities in early-stage growth-oriented companies. Our VP model also identifies an
alignment between Stakeholder value propositions and the knowledge-management learning
emerging from Cross-border operations (see assertions associated with this topic [Topic 6] in
Appendix A). This puts the Stakeholder value propositions element in a central position and
underlines its importance in terms of VP portfolio enhancement and alignment in cross-
border contexts. Interestingly, our topic modelling separates Foreign market entry activities
from Cross-border operations activities, the latter focusing on managing knowledge flow and
learning rather than foreign market development. The emergence of this distinction is an
interesting finding that merits future study.
5.3. How AI Resources and Capabilities Can Enhance the VPs of Early-Stage
Growth-Oriented Companies
Given our findings, we can now elaborate on our central question: how can the VPs of
early-stage growth-oriented companies be enhanced by the adoption of AI resources and
capabilities? Our choice of criteria for the selection of specific assertions was based on the
insights of Mishra and Pani [
28
], Güngör [
36
], Majhi et al. [
30
], and Rogers [
2
] about the
capability of the activities described by these assertions to be enhanced by AI technologies,
more specifically sensing and seizing opportunities and reconfiguring key business aspects
Appl. Sci. 2024,14, 3277 13 of 23
related to customers, competition, data utilization, innovation, and value creation. These
included the following:
•automating business processes;
•gaining decision-making insights through data analysis;
•
enhancing engagement or relationships with customers, employees, investors, part-
ners, and other relevant stakeholders;
•
identifying opportunities in the value chain from the adoption of a multi-stakeholder
benefit analysis perspective;
•
building rich customer prospect profiles, enabling dynamic pricing, and automating
workflows and post-order services;
•
uncovering new customer needs, business opportunities, and corresponding innova-
tive offers.
The actionable insights selected on the basis of the above criteria are shown in Table 2
alongside specific potential benefits of enabling or enhancing them using AI. The analy-
sis was performed in keeping with the logic of our VP enhancement framework model
(Figure 4
). A straightforward examination of the assertions in Table 2suggests that AI
resources and capabilities could indeed enhance VP activities across all parts of the VP
framework, bringing us to the possibility of a quantitative comparison between the degrees
to which the different VP elements could be enhanced by AI. However, our results do
not allow for such a straightforward comparison. Still, we posit being able to achieve
the development of valuable insights about overarching topics with the highest or lowest
potential to be enhanced by AI resources and capabilities.
Based on Table 2, we could claim that there is a clear potential for AI to enhance the four
core elements of our VP model: Value created, Stakeholder value propositions,Foreign market entry,
and Customer base (topics 1 to 4, respectively). More importantly, we find the greatest number
of assertions related to activities that could be enhanced by AI under Topic 4 (Customer
base), which is the one associated with scaling potential. We therefore see the adoption of
AI resources and capabilities as potentially most influential in shaping strategic activities
focusing on growing a company’s customer base (a finding supported by Rogers [2]).
Table 2. VP-related activities that could be enhanced by AI resources and capabilities.
Topic Activities That Could Be Enhanced by AI
Resources and Capabilities Benefits of Using AI
Topic 1:
Value created
•Make decisions on how to best deploy
value-adding combinations of external and
internal resources, while complying with all
relevant cultural, legal, and regulatory norms.
•
Increase order fulfillment and delivery capacity
through partnering with third-party
service providers.
•Provide vendors and suppliers with real-time
analytics on sales required to boost their own
operations efficiency, sales, and profits.
•Demonstrate how your technology, data,
knowledge and experience contribute to the
company’s scaling master plan.
•AI allows firms to identify and align internal
and external resources into value-adding
combinations given a set of specific business
objectives under the limits imposed by legal,
cultural and/or ethical norms. The amount of
data, the analytics, and time requirements are
far more efficient than any other method driven
by humans alone.
•AI can transform the interaction and
connectivity with other firms, thus increasing
the efficiency of their joint operations and the
synergy among different value contributions.
•
Efficiency and productivity gains are two of the
most often-cited benefits of implementing AI
within the enterprise.
•AI can support at least three important
business needs: automating business processes,
gaining insight into new growth opportunities
through data analysis, and engaging with
customers and employees to create an
innovation and growth-oriented culture.
Appl. Sci. 2024,14, 3277 14 of 23
Table 2. Cont.
Topic Activities That Could Be Enhanced by AI
Resources and Capabilities Benefits of Using AI
Topic 2:
Stakeholder value
propositions
•Develop investor VPs describing the path to
ROI in return for investors’ funds
and confidence.
•
Track changes in stakeholders’ VPs and use the
information to realign your VPs to them.
•Continuously improve your VPs based on
results and feedback.
•Learn from VPs of companies that have scaled
early, rapidly, and securely, and use them to
differentiate your company on the market.
•
Develop VPs that enhance your customers’ and
suppliers’ outcomes, marketing strategies, and
competitive advantages.
•
Firms may use AI to augment the mutual value
of the business model by using data generated
by all relevant stakeholders, including the focal
company itself.
•
AI enables shorter development cycles and cuts
the time needed to move from design to
commercialization, leading to more immediate
ROI on development dollars.
•AI offers the opportunity to link real-time
analytics to the quality of stakeholders’ VPs.
•Companies can use AI to monitor online
information about new competitive
developments, changes in VPs of competitors,
industry and market trends that would inform
the refinement of their own VPs.
Topic 3: Foreign
market entry
•Develop a replicable formula to repeat what
worked in one location in other locations.
•Improve links, interactions, and shared
purpose with locals in each region the company
operates in.
•Include local actors in your global
communication system and learn from
multiple local experiences.
•AI offers the opportunity to transform the
quality and the impact of the value-adding
interactions among partners in different global
locations, thus enabling the replicability of
success formulas, innovative ideas, and
competitive insights.
Topic 4:
Customer base
•Apply big data analytics to produce insightful
information about users, suppliers, and
customers to enhance shopping pattern
analysis, improve customer experience, predict
market trends, provide more secure online
payment solutions, increase personalization,
optimize and automate pricing, and provide
dynamic customer service.
•Attract traffic and new customers by targeting
and retargeting users from search engines,
referrals, adds and social media.
•Engage customers to produce testimonials,
reviews and ratings that help new customers to
make purchasing decisions with knowledge of
other customers’ experiences.
•
Provide rewards that satisfy customer needs for
recognition of their loyalty.
•Continuously improve user interfaces and
applications that directly influence the entirety
of the customer experience including
personalized content, quality messaging, and
the delivery and returns process.
•Automatically extract the information a user,
customer, investor, or stakeholder wants from
the vast amount of available information.
•Define the ideal target customer profiles and
engage them relentlessly.
•The customer base topic includes the largest
number of activities that could be enhanced by
AI, all of which are quite self-explanatory.
Appl. Sci. 2024,14, 3277 15 of 23
Table 2. Cont.
Topic Activities That Could Be Enhanced by AI
Resources and Capabilities Benefits of Using AI
•Use an end-to-end solution that links
procurement directly with end customers to
eliminate or reduce inventory and the number
of intermediaries between the company
and customers.
•Enable employees, customers, users, investors,
and other relevant parties to automatically
extract information from company data for the
purpose of decreasing costs and adding value
to other stakeholders.
Topic 5:
Continuous
improvement
•
Continuously improve individuals, operations,
and infrastructures to advance and deliver a
portfolio of innovative offers.
•Expand information about the company, its
offers, its achievements, and its affiliations.
•
Apply processes that continuously improve the
cybersecurity of the company as well as its
offers, channels, and resources.
•Sell online using a variety of online and offline
promotional channels.
•Sell offers the target market perceives to be
better than relevant alternatives.
•Unbundle the value chain and the jobs to be
done within it to outsource lower value tasks to
freelance workers and perform higher value
tasks internally.
•Use data and artificial intelligence to
personalize offers to consumers.
•Organizations can expect a reduction of errors
as well as stronger adherence to established
standards when they add AI technologies to
their business processes.
•Companies are using AI to improve many
aspects of talent management, from
streamlining the hiring process to rooting out
bias in corporate communications.
•Retailers can use AI to better target their
marketing efforts, develop a more efficient
supply chain, and better calculate pricing for
optimal returns. At retail companies where
humans do the majority of the work, AI will
help predict customer requirements and
appropriate staffing levels.
•As an example, the pharmaceutical sector can
use the technology to perform drug-discovery
data analysis and predictions that can’t be done
with conventional technologies.
•
The financial industry can use AI to strengthen
its fraud detection efforts.
•AI’s capacity to take in and process massive
amounts of data in real time means
organizations can implement
near-instantaneous monitoring capabilities that
have the capacity to alert them to issues,
recommend action and, in some cases, to even
initiate a timely response.
•AI lowers the barrier of entry and liability of
foreignness by allowing firms, especially new
ventures, to access information and data about
best strategies to enter markets.
•AI has an impact on the development and
management of global value chains. It can be
used to improve predictions of future trends,
such as changes in consumer demand, and to
better manage risk along the supply chain.
Appl. Sci. 2024,14, 3277 16 of 23
Table 2. Cont.
Topic Activities That Could Be Enhanced by AI
Resources and Capabilities Benefits of Using AI
Topic 6:
Cross-border
operations
•Attain positions in cross-border networks
which provide access to privileged data
and information.
•Build operational cross-border
capabilities early.
•Coordinate, evaluate, and share knowledge
between headquarters, cross-border units, and
among the units themselves.
•Simultaneously develop global learning
capabilities, cross-border flexibility, and
global competitiveness.
•
AI allows businesses to better manage complex
and dispersed production units and improve
the overall efficiency of their global value
chains. For example, business can use AI to
improve warehouse management, demand
prediction, and improve the accuracy of
just-in-time manufacturing and delivery.
Robotics can increase productivity and
efficiency in packing and inventory inspection.
Business can also use AI to improve physical
inspection and maintenance of assets, and
quality of production by different supply
chain partners.
Topic 7: company
brand image
•Expand brand coverage and
eliminate intermediaries.
•Select and retain a consistent identity for the
multiple audiences with which you interact.
•Deploy efficient e-commerce technologies and
automation to reduce company costs and lower
prices of offers.
•AI can collect information about company
image, or problems related to suppliers that
may affect the firm’s reputation and
future operations.
•
AI’s monitoring capabilities can be effective for
monitoring what customers value about the
firm, and even predicting trends that could
impact its wellbeing.
•AI allows brand owners to automatically
humanize their brands, provide better
customer care, improve consumer loyalty, and
expand their reach effectively.
The contents of Table 2also suggest that Continuous improvement may be another VP
element that could be enhanced by AI. Interestingly, this is the VP element under which
falls the assertion that appears to be most strictly related to AI, i.e., using data and artificial
intelligence to personalize offers to consumers. Our VP model shows that Continuous
improvement links Stakeholder value propositions and Value created via the Company image. Two
of the most relevant assertions under value created refer to the ability of firms to choose
to combine their internal resources with those of other resource owners to create value
they could not otherwise create on their own, in the context of complying with all relevant
cultural, legal, and regulatory norms. This speaks directly to the power of AI resources
and capabilities to enable a company’s reconfiguring capacity [
30
]. The potential of AI
in enabling a dynamic Continuous improvement link between Value created (reconfiguring
capacity), Stakeholder value propositions (business ecosystem alignment structure), Foreign
market entry (ability to address new global markets by using insights from successful local
products and services), and companies’ scaling potential (Customer base) is an important
finding that should become the subject of more comprehensive future studies.
Another interesting finding is the seemingly central role of Stakeholder value propositions
(development and alignment) in both our VP framework and the set of activities that could
be enhanced by the adoption of AI resources and capabilities (Table 2). Activities related
to Stakeholder value propositions appear to be uniquely related to Cross-border operations in
addition to their inherent relation with three of the four core VP elements, Foreign market
entry,Value created, and Customer base. These interrelationships are also part of the positive
loop enabled by the Continuous improvement VP element. Our analysis suggests that the
adoption of AI resources and capabilities can enable and strengthen these interrelations.
Appl. Sci. 2024,14, 3277 17 of 23
6. Conclusions
The objective of this article was to explore how AI can help the enhancement of the VPs
of early-stage growth-oriented companies. Therefore, we have suggested an actionable VP
framework providing a structured business activity system that could enable both scholars
and business practitioners involved in growth-oriented companies to examine the potential
beneficial impact of AI resources and capabilities on their VPs. Based on the literature
pertaining to AI-based business value, VP frameworks, and business ecosystems, we gener-
ated a corpus of assertions referring to actionable insights and applied topic modelling to
identify seven interrelated groups of activities that were labelled as Value created,Stakeholder
value propositions,Foreign market entry,Customer base,Continuous improvement,Cross-border
operations, and Company image. The link between Value created and Stakeholder value proposi-
tions could be considered as a core value creation axis that could be strengthened by Foreign
market entry and Cross-border business activities to enable Customer base growth. At the same
time, the company’s Customer base growth is continuously enhanced through a positive
loop enabled by the Continuous improvement of the value creation process, the alignment of
Stakeholder value propositions, the Foreign market entry efforts and the overall Company image.
The suggested framework is not just conceptual but practically actionable, since it comes
with a structured system of activities related to each of the seven generic VP elements.
Each of the VP elements (groups of activities) was then examined in terms of its capac-
ity to be enhanced by the adoption of AI resources and capabilities. Thus, the study makes
a methodological contribution by demonstrating a way of applying topic modelling to
turn research assertions into actionable insights for entrepreneurs and executive managers.
The second main contribution is the actionable framework itself since, to the best of our
knowledge, there has not been any framework published that could explicitly address the
opportunity to enhance VPs by the adoption of AI resources and capabilities.
The study of how AI affects the VPs and growth of early-stage growth-oriented compa-
nies is highly relevant independently of the economic sector, and needs more attention by
researchers. A necessary next step in the advancement of this line of research would be the
adoption of the proposed VP enhancement framework to examine the practices of real-life
companies. It is, however, outside of the scope of the present article and will be considered
in future works. Future empirical studies should also address other important questions,
the answers to which, unfortunately, are also out of the scope of the present study:
•
How does AI enable early-stage growth-oriented companies to analyze large volumes
of data for the development of VP-related insights that could result in distinctive
competitive advantage?
•
What are the implications of AI for the scalability and growth potential of such com-
panies in different industries? How does the specific nature of the industry affect the
scaling and growth potential?
•
What skills and resources are necessary for new companies to effectively implement
AI initiatives? How are these skills and resources related to the skills and resources
required for the articulation and delivery of competitive VPs of growth-oriented firms?
•
What role does AI play in business analytics and forecasting for early-stage growth-
oriented companies, particularly in industries with high uncertainty? Can AI reduce the
uncertainty in decision making relevant to strategic and competitive VP enhancement?
Finally, we should emphasize again that this article sought to contribute to an emerging
stream in entrepreneurship and innovation management research which calls for a stronger
focus on the development of actionable principles for practitioners [
21
]. It highlights the
need for a distinct body of knowledge around pragmatically oriented, actionable principles
that could bridge the gap between the causal mechanisms of entrepreneurship theory
and the complex realities of entrepreneurial practice. Our focus on actionable insights is
ultimately an expression of our commitment to the cause of applied research articulated by
Berglund et al. [21] and Shepherd and Gruber [75].
Appl. Sci. 2024,14, 3277 18 of 23
Author Contributions: Conceptualization, S.T., C.K. and T.B; methodology, T.B., S.T. and C.K.;
software modelling, S.T. and T.B.; validation, D.H., S.T., T.B. and C.K.; formal analysis, S.T., C.K. and
D.H.; investigation, S.T., C.K. and T.B.; resources, S.T., T.B. and C.K.; data curation, S.T.; writing—
original draft preparation, S.T. and C.K.; writing—review and editing, C.K. and S.T.; visualization,
S.T.; project administration, S.T. All authors have read and agreed to the published version of
the manuscript.
Funding: This research received no external funding.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available on request from the
corresponding author.
Acknowledgments: The authors express their sincere gratitude to Michael Weiss from the Technology
Innovation Management Program at Carleton University, Ottawa, ON, Canada, for the access to the
Topic Model Explorer tool: https://github.com/michaelweiss/topic-model-explorer accessed on
6 April 2024.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A Topic Modelling Results (Including Most Frequent Words and Assertions)
(1)
Topic 1—VC—Value created (product, service, sale, investor, norm, scaling master
plan, benefit, resource)
•
Identify and access resources that allow scaling at lower costs by creating benefits for
the resource owners that they cannot create alone.
•Allow resource owners to make money using your products and services.
•
Align value created from the combination and deployment of external resources with
scaling master plan.
•
Combine company resources with those of other resource owners to create value that
cannot be created by your company alone.
•
Make decisions on how to best deploy value-adding combinations of external and inter-
nal resources, while complying with all relevant cultural, legal, and regulatory norms.
•
Establish partnerships that increase the demand for, and complement, your products.
•Enable your freelance workers to become world-class service providers.
•
Increase order fulfillment and delivery capacity through partnering with third-party
service providers and acquiring drop-shipping facilities.
•
Provide investors with evidence that your business model and target market can
generate enough sales for the company to be investable in.
•
Provide returns that leave the most important resource owners better off than they
would have been if they had not engaged with you.
•Simplify the complementarity of your company products and services.
•
Combine two or more resources to create value that exceeds the sum of the value
created from each resource separately.
•
Provide vendors and suppliers with real-time analytics on sales required to boost their
own operational efficiency, sales and profits.
•
Use the company’s technical and knowledge expertise to develop prototypes that
demonstrate the value of your products and services.
•
Demonstrate how your technology, knowledge and experience contribute to the com-
pany’s scaling master plan.
•
Contribute to university research projects and enhance the academic and technical
reputation of your team to ensure that your company’s proposed technological concept
is valid.
•
Develop a compelling image of your future company and use it to convince investors
to provide funding and resource owners to provide resources needed to scale.
(2)
Topic 2—SVP—Stakeholder value propositions (value proposition, vision, future,
salesperson, path)
Appl. Sci. 2024,14, 3277 19 of 23
•
Instill a sense of purpose by articulating and pursuing a compelling vision for the
company in the future.
•
Provide investors a compelling short-term financial VP and a vision of favourable
medium- and long-term VPs.
•
Develop VPs that enhance employee satisfaction, psychological attachment, and
behavioural commitment to your company.
•Continuously improve VPs based on results and feedback.
•
Learn from VPs of companies that have scaled early, rapidly, and securely, and use
them to differentiate your company on the market.
•
Track changes in stakeholders’ VPs and use the information to realign your VPs
to them.
•
Develop VPs for key members of the value chain that align with key members’ VPs
and improve the competence of the value chain.
•
Develop VPs that enhance your customers’ and suppliers’ outcomes, marketing strate-
gies, and competitive advantages.
•
Develop investor VPs describing the path to return on investment in return for in-
vestors’ funds and confidence.
(3)
Topic 3—FME—Foreign market entry (local, prototype, alliance, patent, regulation)
•
Develop a replicable formula to repeat what worked in one location in other locations.
•
Promote the company’s achievements to date, e.g., awards, high-profile endorsements
from established companies, sales, successful alliances and interactions with potential
customers, strength of the company’s scaling master plan and the company’s potential
to create local jobs, contributions to social causes, and advancement of knowledge.
•Enable locals in other locations to succeed because of your company.
•
Enter a different geographical market by partnering with or purchasing local companies.
•
Improve links, interactions, and shared purpose with locals in each region the company
operates in.
•
To globalize a local product, develop core product that can be readily disseminated
worldwide.
•Integrate local innovation into global themes, products, and services.
•
Include local actors in your global communication system and learn from multiple
local experiences.
(4)
Topic 4—CB—Customer base (customer, stakeholder, user, supplier, loyalty, referral)
•
Apply big data analytics to produce insightful information about users, suppliers and
customers to enhance shopping pattern analysis, improve customer experience, predict
market trends, provide more secure online payment solutions, increase personalization,
optimize and automate pricing, and provide dynamic customer service.
•
Attract traffic and new customers by targeting and retargeting users from search
engines, referrals, adds and social media.
•
Engage customers to produce testimonials, reviews and ratings that help new cus-
tomers to make purchasing decisions with knowledge of other customers’ experiences.
•Provide rewards that satisfy customer needs for recognition of their loyalty.
•
Continuously improve user interfaces and applications that directly influence the
entirety of customer experience including personalized content, quality messaging,
and the delivery and returns process.
•
Implement a stakeholder-centric approach to satisfy stakeholder expectations in
all markets.
•
Automatically extract the information a user, customer, investor, or stakeholder wants
from the vast amount of available information.
•
Build internet-based capabilities to acquire and retain customers during the initial
stages of the company’s life cycle.
•Define the ideal target customer profiles and engage them relentlessly.
Appl. Sci. 2024,14, 3277 20 of 23
•
Use an end-to-end solution that links procurement directly with end customers to
eliminate or reduce inventory and the number of intermediaries between the company
and customers.
•
Enable employees, customers, users, investors, and other relevant parties to automati-
cally extract information from company data for the purpose of decreasing costs and
adding value to other stakeholders.
•
Establish trust and positive rapport with your customers that leads to long-term,
mutually beneficial business relationships.
•
Adjust to your customers’ moods and work to find common ground to build familiarity.
•
Listen to your customers, take their feedback seriously, and adjust operations as needed.
•
Digitize as much of your company as you can to create value for customers, reduce
costs, and increase security.
•Deliver better performance on the metrics that customers care about.
(5)
Topic 5—CI—Continuous improvement (offer, channel, artificial intelligence, thresh-
old, value chain, data)
•
Continuously improve individuals, operations, and infrastructures to advance and
deliver a portfolio of innovative offers.
•Apply processes that make offers easier to understand, produce, and deliver.
•Invest to improve cybersecurity of the value chain.
•
Expand information about the company, its offers, its achievements, and its affiliations.
•
Apply processes that continuously improve the cybersecurity of the company as well
as its offers, channels, and resources.
•Adapt offers to each market.
•Provide a variety of complementary offers to each market.
•Sell online using a variety of online and offline promotional channels.
•Sell offers the target market perceives to be better than relevant alternatives.
•Strengthen cybersecurity attributes of offers compared to competitors.
•Use scientific and technological advances to develop innovative offers.
•Broaden company offers to address more customer jobs to be done.
•
Unbundle the value chain and the jobs to be done within it to outsource lower value
tasks to freelance workers and perform higher value tasks internally.
•Use data and artificial intelligence to personalize offers to consumers.
(6)
Topic 6—CBO—Cross-border operations (community, founder, regulation, cross border)
•
Attain positions in cross-border networks which provide access to privileged information.
•Build operational cross-border capabilities early.
•
Coordinate, evaluate, and share knowledge between headquarters and cross-border
units, and among the units themselves.
•
Simultaneously develop global learning capabilities, cross-border flexibility, and
global competitiveness.
•
Build a community where each member supports others rather than building many
separate businesses.
•
Contribute to the creation of public goods such as open source code, standards, and
test beds.
(7) Topic 7—IMG—Company image (brand, identity, competitor, competition, e-commerce,
price)
•Create a unique brand identity to differentiate from the competition.
•Expand brand coverage and eliminate intermediaries.
•Brand the company and build a brand that has a strong market presence.
•
Select and retain a consistent identity for the multiple audiences with which you interact.
•
Deploy efficient e-commerce technologies and automation to reduce company costs
and lower prices of offers.
•Sell high-quality offers at lower prices compared to competitors.
Appl. Sci. 2024,14, 3277 21 of 23
References
1.
Sjödin, D.; Parida, V.; Palmié, M.; Wincent, J. How AI capabilities enable business model innovation: Scaling AI through
co-evolutionary processes and feedback loops. J. Bus. Res. 2021,134, 574–587. [CrossRef]
2.
Rogers, D. The Digital Transformation Playbook—Rethink Your Business for the Digital Age; Columbia University Press: New York,
NY, USA, 2016.
3.
Wagner, D.N. The nature of the Artificially Intelligent Firm—An economic investigation into changes that AI brings to the firm.
Telecommun. Policy 2020,44, 101954. [CrossRef]
4.
Schallmo, D.; Williams, C.; Boardman, L. Digital Transformation Of Business Models—Best Practice, Enablers, And Roadmap. Int.
J. Innov. Manag. 2017,21, 1740014. [CrossRef]
5.
Nambisan, S.; Zahra, S.; Luo, Y. Global platforms and ecosystems: Implications for international business theories. J. Int. Bus.
Stud. 2019,50, 1464–1486. [CrossRef]
6.
Schallmo, D.; Tidd, J. Digitalization: Approaches, Case Studies, and Tools for Strategy, Transformation and Implementation; Series on
Management for Professionals; Springer: New York, NY, USA, 2021.
7.
Niemand, T.; Coen Rigtering, J.P.; Kallmünzer, A.; Kraus, S.; Maalaoui, A. Digitalization in the financial industry: A contingency
approach of entrepreneurial orientation and strategic vision on digitalization. Eur. Manag. J. 2021,39, 317–326. [CrossRef]
8.
Soluk, J.; Miroshnychenko, I.; Kammerlander, N.; De Massis, A. Family Influence and Digital Business Model Innovation: The
Enabling Role of Dynamic Capabilities. Entrep. Theory Pract. 2021,45, 867–905. [CrossRef]
9.
Verhoef, P.; Broekhuizen, T.; Bart, Y.; Bhattacharya, A.; Qi Dong, J.; Fabian, N.; Haenlein, M. Digital transformation: A
multidisciplinary reflection and research agenda. J. Bus. Res. 2021,122, 889–901. [CrossRef]
10.
Kraus, S.; Jones, P.; Kailer, N.; Weinmann, A.; Chaparro-Banegas, N.; Roig-Tierno, N. Digital Transformation: An Overview of the
Current State of the Art of Research. SAGE Open 2021,11, 1–15. [CrossRef]
11.
Crane, D. It’s Time for Canada to Focus on the Scale-Up Challenge. IT World Canada. 2019. Available online: https://www.
itworldcanada.com/article/its-time-for-canada-to-focus- on-the-scale-up-challenge/420027 (accessed on 6 April 2024).
12.
Bailetti, T.; Tanev, S.; Keen, C. What Makes Value Propositions Distinct and Valuable to New Companies Committed to Scale
Rapidly? Technol. Innov. Manag. Rev. 2020,10, 14–27. [CrossRef]
13.
Bussgang, J.; Stern, O. How Israeli Startups Can Scale. Harv. Bus. Rev. 2015. Available online: https://hbr.org/2015/09/how-
israeli-startups-can-scale (accessed on 6 April 2024).
14.
Girotra, K.; Netessine, S. OM Forum—Business Model Innovation for Sustainability. Manuf. Serv. Oper. Manag. 2013,15, 537–544.
[CrossRef]
15.
Baldassarre, B.; Calabretta, G.; Bocken, N.; Jaskiewicz, T. Bridging sustainable business model innovation and user-driven
innovation: A process for sustainable value proposition design. J. Clean. Prod. 2017,147, 175–186. [CrossRef]
16.
Payne, A.; Frow, P.; Eggert, A. The customer value proposition: Evolution, development, and application in marketing. J. Acad.
Mark. Sci. 2017,45, 467–489. [CrossRef]
17.
Lanning, M. Try taking your value proposition seriously—Why delivering winning value propositions should be but usually is
not the core strategy for B2B (and other businesses). Ind. Mark. Manag. 2020,87, 306–308. [CrossRef]
18. Amit, R.; Zott, C. Business Model Innovation Strategy; John Wiley & Sons: Hoboken, NJ, USA, 2021.
19.
Onetti, A.; Zucchella, A.; Jones, M.; McDougall-Covin, P. Internationalization, innovation and entrepreneurship: Business models
for new technology-based firms. J. Manag. Gov. 2012,16, 337–368. [CrossRef]
20. Winter, S. Understanding dynamic capabilities. Strateg. Manag. J. 2003,24, 991–995. [CrossRef]
21.
Berglund, H.; Dimov, D.; Wennberg, K. Beyond bridging rigor and relevance: The three-body problem in entrepreneurship. J. Bus.
Ventur. Insights 2018,9, 87–91. [CrossRef]
22. Blei, D. Probabilistic Topic Models. Commun. ACM 2012,55, 77–84. [CrossRef]
23.
Alghamdi, R.; Alfalqi, K. A survey of topic modeling in text mining. International J. Adv. Comput. Sci. Appl. (IJACSA) 2015,6,
147–153. [CrossRef]
24.
Hannigan, T.; Haans, R.; Vakili, K.; Tchalian, H.; Glaser, V.; Wang, M.; Kaplan, S.; Jennings, P. Topic Modeling in Management
Research: Rendering New Theory from Textual Data. Acad. Manag. Ann. 2019,13, 586–632. [CrossRef]
25.
Lu, Q.; Chesbrough, H. Measuring open innovation practices through topic modelling: Revisiting their impact on firm financial
performance. Technovation 2022,114, 102434. [CrossRef]
26.
Thakral, P.; Sharma, D.; Ghosh, K. Evidence-based knowledge management: A topic modeling analysis of research on knowledge
management and analytics. VINE J. Inf. Knowl. Manag. Syst. 2024; ahead-of-print. [CrossRef]
27.
Makadok, R.; Burton, R.; Barney, J. A practical guide for making theory contributions in strategic management. Strateg. Manag. J.
2018,39, 1530–1545. [CrossRef]
28.
Mishra, A.; Pani, A. Business value appropriation roadmap for artificial intelligence. VINE J. Inf. Knowl. Manag. Syst. 2020,51,
2059–5891. [CrossRef]
29. Davenport, T.; Ronanki, R. Artificial Intelligence for the Real World. Harv. Bus. Rev. 2018, 108–116.
30.
Majhi, S.G.; Mukherjee, A.; Anand, A. Business value of cognitive analytics technology: A dynamic capabilities perspective.
VINE J. Inf. Knowl. Manag. Syst. 2021; ahead-of-print. [CrossRef]
31.
Teece, D. Explicating dynamic capabilities: The nature and micro foundations of (sustainable) enterprise performance. Strateg.
Manag. J. 2007,28, 1319–1350. [CrossRef]
Appl. Sci. 2024,14, 3277 22 of 23
32.
Nenonen, S.; Storbacka, K.; Windahl, C. Capabilities for market-shaping: Triggering and facilitating increased value creation. J.
Acad. Mark. Sci. 2019,47, 617–639. [CrossRef]
33.
Berman, S.; Dalzell-Payne, P. The Interaction of Strategy and Technology in an Era of Business Re-Invention. Strategy Leadersh.
2018,46, 10–15. [CrossRef]
34. Huang, M.-H.; Rust, R. Engaged to a Robot? The Role of AI in Service. J. Serv. Res. 2021,24, 30–41. [CrossRef]
35.
Paschen, J.; Wilson, M.; Ferreira, J. Collaborative intelligence: How human and artificial intelligence create value along the B2B
sales funnel. Bus. Horiz. 2020,63, 403–414. [CrossRef]
36.
Güngör, H. Creating Value with Artificial Intelligence: A Multi-stakeholder Perspective. J. Creat. Value 2020,6, 72–85. [CrossRef]
37.
Zawadzki, J. Introducing the AI Project Canvas. Medium. 2020. Available online: https://towardsdatascience.com/introducing-
the-ai-project-canvas-e88e29eb7024 (accessed on 6 April 2024).
38.
Kerznel, U. Enterprise AI Canvas Integrating Artificial Intelligence into Business. Appl. Artif. Intell. 2021,35, 1–12. Available
online: https://arxiv.org/pdf/2009.11190.pdf (accessed on 6 April 2024). [CrossRef]
39. Anderson, J.; Narus, J.; Van Rossum, W. Customer VPs in business markets. Harv. Bus. Rev. 2006,84, 91–99.
40. Frow, P.; Payne, A. A stakeholder perspective of the value proposition concept. Eur. J. Mark. 2011,45, 223–240. [CrossRef]
41.
Payne, A.; Frow, P.; Steinhoff, L.; Eggert, A. Toward a comprehensive framework of value proposition development: From
strategy to implementation. Ind. Mark. Manag. 2020,87, 244–255. [CrossRef]
42.
Lanning, M. Delivering Profitable Value: A Revolutionary Framework to Accelerate Growth, Generate Wealth, and Rediscover the Heart of
Business; Perseus Press: Cambridge, MA, USA, 2000.
43.
Webster, F. Market-Driven Management: How to Define, Develop and Deliver Customer Value, 2nd ed.; John Wiley & Sons: Hoboken,
NJ, USA, 2002.
44.
Ballantyne, D.; Frow, P.; Varey, R.; Payne, A. VPs as communication practice: Taking a wider view. Ind. Mark. Manag. 2011,40,
202–210. [CrossRef]
45.
Eggert, A.; Ulaga, W.; Frow, P.; Payne, A. Conceptualizing and communicating value in business markets: From value in exchange
to value in use. Ind. Mark. Manag. 2018,69, 80–90. [CrossRef]
46.
Kothandaraman, P.; Wilson, D. The future of competition: Value-creating networks. Ind. Mark. Manag. 2001,30, 379–389.
[CrossRef]
47. Lanning, M.; Michaels, E. A business is a value delivery system. McKinsey Staff Pap. 1988,41, 1–16.
48.
Wouters, M.; Anderson, J.; Kirchberger, M. New-Technology Startups Seeking Pilot Customers: Crafting a Pair of VPs. Calif.
Manag. Rev. 2018,60, 101–124. [CrossRef]
49.
Holttinen, H. Contextualizing value propositions: Examining how consumers experience value propositions in their practices.
Australas. Mark. J. 2014,22, 103–110. [CrossRef]
50.
Bocken, N.; Snihur, Y. Lean Startup and the business model: Experimenting for novelty and impact. Long Range Plan. 2020,
53, 101953. [CrossRef]
51.
Schepis, D. How innovation intermediaries support start-up internationalization: A relational proximity perspective. J. Bus. Ind.
Mark. 2020,36, 2062–2073. [CrossRef]
52.
Grönroos, C. A service perspective on business relationships: The value creation, interaction and marketing interface. Ind. Mark.
Manag. 2011,40, 240–247. [CrossRef]
53.
Skålén, P.; Gummerus, J.; von Koskull, C.; Magnusson, P. Exploring value propositions and service innovation: A service-dominant
logic study. J. Acad. Mark. Sci. 2015,43, 137–158. [CrossRef]
54. Zott, C.; Amit, R.; Massa, L. The Business Model: Recent Developments and Future Research. J. Manag. 2011,37, 1019–1042.
55.
Zhang, J.; Lichtenstein, Y.; Gander, J. Designing scalable digital business model. Business models and modelling. Adv. Strateg.
Manag. 2015,33, 241–277.
56.
Shepherd, D.; Souitaris, V.; Gruber, M. Creating New Ventures: A Review and Research Agenda. J. Manag. 2020,47, 11–42.
[CrossRef]
57.
Rydehell, H.; Löfsten, H.; Isaksson, A. Novelty-oriented value propositions for new technology-based companies: Impact of
business networks and growth orientation. J. High Technol. Manag. Res. 2018,29, 161–171. [CrossRef]
58. Malnight, T.; Buche, I.; Dhanaraj, C. Put Purpose at the CORE of Your Strategy. Harv. Bus. Rev. 2019,97, 70–78.
59.
Kier, A.; McMullen, J. Entrepreneurial imaginativeness in new venture ideation. Acad. Manag. J. 2018,61, 2265–2295. [CrossRef]
60. Eisenmann, T.R. Entrepreneurship: A working definition. Harv. Bus. Rev. 2013, 1–3.
61.
Casali, G.; Perano, M.; Presenza, A.; Abbate, T. Does innovation propensity influence wineries’ distribution channel decisions?
Int. J. Wine Bus. Res. 2018,30, 446–462. [CrossRef]
62.
Kollmann, T.; Stöckmann, C.; Kensbock, J.M. Fear of failure as a mediator of the relationship between obstacles and nascent
entrepreneurial activity—An experimental approach. J. Bus. Ventur. 2017,32, 280–301. [CrossRef]
63. Adner, R. Ecosystem as structure: An actionable construct for strategy. J. Manag. 2017,43, 39–58. [CrossRef]
64.
Dattée, B.; Alexy, O.; Autio, E. Maneuvering in Poor Visibility: How Firms Play the Ecosystem Game when Uncertainty is High.
Acad. Manag. J. 2018,61, 466–498. [CrossRef]
65.
Akemu, O.; Whiteman, G.; Kennedy, S. Social enterprise emergence from social movement activism: The Fairphone case. J. Manag.
Stud. 2016,53, 846–877. [CrossRef]
66. Jacobides, M.G.; Cennamo, C.; Gawer, A. Towards a theory of ecosystems. Strateg. Manag. J. 2018,39, 2255–2276. [CrossRef]
Appl. Sci. 2024,14, 3277 23 of 23
67.
Bailetti, T.; Tanev, S. Examining the Relationship between Value Propositions and Scaling Value for New Companies. Technol.
Innov. Manag. Rev. 2020,10, 5–13. [CrossRef]
68.
Tanev, S.; Bailetti, T.; Keen, C.; Hudson, D. The Potential of AI to Enhance the Value Propositions of New Companies Committed
to Scale Early and Rapidly. In Artificial Intelligence and Innovation Management; Series on Technology Management; Tanev, S.,
Blackbright, H., Eds.; World Scientific: Singapore, 2022. [CrossRef]
69.
Frow, P.; McColl-Kennedy, J.; Hilton, T.; Davidson, A.; Payne, A.; Brozovic, D. Value propositions: A service ecosystems
perspective. Mark. Theory 2014,14, 327–351. [CrossRef]
70.
Kristensen, H.S.; Remmen, A. A framework for sustainable value propositions in product-service systems. J. Clean. Prod. 2019,
223, 25–35. [CrossRef]
71.
Mishra, S.; Ewing, M.T.; Pitt, L.F. The effects of an articulated customer value proposition (CVP) on promotional expense, brand
investment and firm performance in B2B markets: A text based analysis. Ind. Mark. Manag. 2019,87, 264–275. [CrossRef]
72.
Truong, Y.; Simmons, G.; Palmer, M. Reciprocal value propositions in practice: Constraints in digital markets. Ind. Mark. Manag.
2012,41, 197–206. [CrossRef]
73.
Boyd-Graber, J.; Mimno, D.; Newman, D. Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements. In
Handbook of Mixed Membership Models and Its Applications; Airoldi, E.M., Blei, D., Erosheva, E., Fienberg, S., Eds.; Chapman and
Hall/CRC: New York, NY, USA, 2014; pp. 225–254.
74.
Anandarajan, M.; Hill, C.; Nolan, T. Chapter 6. Semantic Space Representation and Latent Semantic Analysis. In Practical Text
Analytics: Maximizing the Value of Text Data; Springer: New York, NY, USA, 2019; pp. 77–92.
75.
Shepherd, D.; Gruber, M. The Lean Startup Framework: Closing the Academic–Practitioner Divide. Entrep. Theory Pract. 2021,45,
967–998. [CrossRef]
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