The Role of Analytics in Data-Driven Business Models of Multi-Sided Platforms: An exploration in the food industry

Abstract

The collection and use of data play an increasingly important role in the growth and success of today’s digital multi-sided platforms (MSPs). However, many aspiring MSPs lack effective strategies for using data to establish a profitable data-driven business model (DDBM). This study explores how MSPs in the food industry can utilize data to develop such a DDBM. Based on an analysis of seven illustrative cases of high-growth MSPs, namely food delivery and meal kit providers, the study identifies eight factors that reveal the role of analytics in those firms’ DDBM, and further classifies them into three DDBM boosters. The findings contribute to our extant knowledge on MSPs and DDBM by addressing how digital platforms in the food industry can leverage big data to
optimize their current business processes, predict future value of their product and service offerings, and develop their partnerships.

Full text

sustainability of MSPs (Trabucchi et al., 2017). Previous
literature shows that apart from revenue growth and cost
optimization, data analytics can decrease customer
acquisitions costs, retain valuable customers, help
predict customer behaviour, improve customer
experience, reduce fraud, provide real time offers, and
enhance decision making (McAfee & Brynjolfsson, 2012;
Redman, 2015; Wamba et al., 2017). However, data on its
own is not a source of competitive advantage since all
firms can collect hordes of data from a variety of sources.
Rather, data must be purposely analyzed, and activated.
Nonetheless, firms face a host of issues - organizational,
financial, physical, and human resources - in their
attemps to create a competitive capability from the use
of data (Gupta & George, 2016; Ghasemaghaei, 2018),
and may easily fail to exploit the benefits of data
analytics (Erevelles et al., 2016).
Despite DDBM and data monetization being of high
interest to companies (Moro Visconti et al., 2017) and
the recent increase of scholarly studies in this domain
(Amado et al., 2018; Fiorini et al., 2018), research
conducted involving factors that characterize data-based
value creation and its role in companies’ business
Data-driven business models (DDBMs) are either
emergent or new multi-layered, multi-dimensional
business models enabled by big data(Hartmann et al.,
2016). Several highly diverse industries are moving
towards DDBM to survive and compete. Such
industries include especially those in which
understanding user-buying patterns in an in-depth
manner is becoming increasingly important, such as
online retailers, the publishing industry, and the
financial and insurance service sectors (Brownlow et
al., 2015; Zaki et al., 2015). More and more, big data
and data analytics play an enabling role in the growth
and success of multi-sided platform (MSP) firms,
which are digital platforms connecting and serving two
or more stakeholders (Evans, 2003; Hagiu, 2006, 2015;
Rochet & Tirole, 2006). The MSP strategy has been
fundamental to the emergence of many of today’s
leading digital businesses from Apple and Google to
AirBnB and Uber (Ikeda & Marshall, 2019).
The analytics, use, and monetization of data are
increasingly crucial for the profitability and
The Role of Analytics in Data-Driven Business
Models of Multi-Sided Platforms: An exploration
in the food industry
Diane Isabelle, Mika Westerlund, Mohnish Mane and Seppo Leminen
The collection and use of data play an increasingly important role in the growth and success of
today’s digital multi-sided platforms (MSPs). However, many aspiring MSPs lack effective strategies
for using data to establish a profitable data-driven business model (DDBM). This study explores
how MSPs in the food industry can utilize data to develop such a DDBM. Based on an analysis of
seven illustrative cases of high-growth MSPs, namely food delivery and meal kit providers, the
study identifies eight factors that reveal the role of analytics in those firms’ DDBM, and further
classifies them into three DDBM boosters. The findings contribute to our extant knowledge on
MSPs and DDBM by addressing how digital platforms in the food industry can leverage big data to
optimize their current business processes, predict future value of their product and service
offerings, and develop their partnerships.
It's not what you look at that matters, it's what you see. Henry David Thoreau
American philosopher

models are lacking (Lim et al., 2018). In particular,
empirical studies on the potential of DDBM innovation
in digital platforms to create and appropriate value
from big data (Clarke, 2016) and overcome value
creation barriers (Lim et al., 2018) are scarce. Given
that MSPs constitute an increasingly important
business strategy in today’s digital economy, there is
an urgent need for a better understanding and more
comprehensive view of the role of analytics in
successful MSP firms’ business models.
Our research question for this paper is as follows: How
can MSPs successfully establish a new DDBM or
strategically shift their current business model to a
DDBM through the use of data analytics? To explore
this question, we selected the food industry for our
investigation, specifically, food delivery and meal kit
providers, which is an under-investigated yet growing
subcategory of MSPs sharing quite similar business
models (Pigatto et al., 2017). At the same time, this
highly capital-intensive industry is faced with some
challenging issues: customer acquisition costs tend to
be very high, while customer retention is generally low,
and both supply-chain and logistics are often costly.
These challenges can rapidly lead to unprofitable
business models even though customer demand for
food MSPs is growing. Firms in that industry are
generally funded by investors; therefore, it is
imperative that their business models generate
sustainable results and profits (Ladd, 2018). Hence,
this industry represents a particularly fertile area for
investigating DDBMs.
Drawing from Lim et al.'s (2018) framework, the
objective of this study is to identify essential factors
that characterize data-based value creation and its role
in DDBM in the food delivery and meal kit industry,
through the use of an illustrative case methodology. In
so doing, we identify eight key factors that illustrate the
role of data analytics in DDBM of successful food MSPs
and advance the theoretical concept of “boosters”
(Leminen et al., in press), with a study of three
boosters that enable successful DDBMs in the food
industry. The contributions of the present study to the
nascent body of knowledge on DDBMs for digital
platforms are as follows. The research, 1) enhances our
understanding of how MSPs in the food industry can
utilize data analytics to develop a DDBM, 2) fills a gap
between big data acquisition and data-based value
creation, and 3) provides managers in the food
industry with a comprehensive and applicable
approach for developing a data-driven model and
integrating it with their MSP strategy to successfully
achieve a transformation toward a DDBM.
Big data is defined by five key attributes, commonly
referred to as the Five Vs: Volume, Variety, Velocity,
Value, and Veracity (McAfee & Brynjolfsson, 2012; White,
2012; Leventhal, 2013; Fiorini et al., 2018). Value is
considered the most important of these attributes
(Hmoud et al., 2017). Value can be financial (for
example, increased revenue and reduced costs) or
intangible (for example, improved customer satisfaction
and informed strategic decisions), or a combination of
both. While the other four attributes stress data
collection, the creation and appropriation of value
defines the potential and means for monetization or
benefitting from data (Lim et al., 2018). Of note, two
recent information technology trends have enabled
companies to obtain more value from data: business
intelligence and analytics, along with cloud computing
(Moro Visconti et al., 2017).
Big data can be classified into three higher level types,
namely, structured, semi-structured, and unstructured.
Approximately 80 percent of the world’s data is
unstructured (Balducci & Marinova, 2018; Sun & Huo,
2019). Hence, big data often means high volumes of
heterogeneous data, which brings unprecedented
opportunities to benefit from that data. In fact, previous
literature has found that firms using analytics are 36
more likely to surpass their competitors in revenue
growth and operating efficiency (Marshall et al., 2015),
and can decrease their customer acquisition costs by
47 (Wamba et al., 2017).
Redman (2015) identified four types of DDBM: 1) pure
content provision, such as Bloomberg corporation; 2)
informationalization, which is building data customers
need, for example, Waze for route guidance; and 3)
infomediation, that is helping people find the data they
need, for example Google. The potentially most
profitable model looks to become 4) data-driven, by
using more and better data to improve strategic and
operational decision making, which is the business
model of our selected meal kit and food delivery
industry.
Engelbrecht and colleagues (2016) argue that innovating
business models from a data-driven perspective is
crucial to long-term success, while de Oliveira &
Cortimiglia (2017) believe that monetization should
The Role of Analytics in Data-Driven Business Models of Multi-Sided Platforms:
An exploration in the food industry Diane Isabelle, MikaWesterlund, Mohnish Mane and
Seppo Leminen

focus on the scalable parts of the business model.
Accordingly, firms can use big data, including user-
generated data, to develop new business models,
update and customize existing offerings, and integrate
business partners in future business models
(Hartmann et al., 2016; Dubé et al., 2018). Without a
doubt, the strategic use of data is fast becoming one of
the key pillars for successful digital platform business
models (Ikeda et al., 2019).
Digital platform businesses have been explored in the
network externalities literature (Katz & Shapiro, 1985).
They enable co-creating value among distinct user
groups through an intermediary who can internalize
network externalities associated with these groups
(Evans, 2003; Zott & Amit, 2010). Hence,
conceptualizing a strong value proposition becomes
even more complex, as it requires an understanding
and management of several needs and objectives
across a network of multiple stakeholders to result in
creating shared value (Porter & Kramer, 2011;
Baldassarre et al., 2017).
In spite of the growth of data, along with the trends in
digital business models and expected benefits from
DDBMs, a recent global survey of ~400 companies
showed that 77 of companies do not have strategies
to use big data effectively (Wang et al., 2015). Many
companies are thus failing to benefit from integrating
big data into their business models (Andersen &
Bjerrum, 2016). The literature offers several reasons for
such failures. According to Morabito (2015), big data
emphasizes ‘utility from’ data rather than ‘ownership
of’ data. This means that access to purposeful data is
key. Further, raw data is useless unless it is purposely
analyzed (Morabito 2015; Gupta & George, 2016). Jones
(2019) notes that there is a difference between data that
can be recorded and data that actually gets recorded, as
well as between the results from data analyses that get
extracted, understood, and exploited for business
benefits. Companies also often lack data analysis
competencies (Koskinen, 2018).
Vidgen et al. (2017) summarize the top five data strategy
issues to overcome: 1) availability of data, 2) using
analytics for improved decision making, 3) managing
data quality, 4) creating a big data and analytics strategy,
and 5) building data skills in the organization.
Compounding these issues, business managers must
also consider privacy and security concerns, as well as
growing regulations (Wong, 2012; Blazquez et al., 2018),
and continually develop their business models over time
(Muzellec et al., 2015). Not surprisingly, few companies
have succeeded in leveraging data and creating a
successful DDBM (Mathis & Köbler, 2016) by linking
analytics and big data for value capture (Trabucchi et al.,
2017).
More research is needed to provide organizational
managers with guidance in these areas (Sorescu, 2017),
as evidenced by the gaps in the literature between big
data and value creation (Vidgen et al., 2017; Lim et al.,
2018). Hence, our objective is to identify key factors that
enable multi-sided digital platforms in the meal kit and
food delivery industry either to successfully establish or
revise their current business model into a DDBM. We
draw from Lim and colleagues' (2018) framework for
data-based value creation in information-intensive
Research overview
The Role of Analytics in Data-Driven Business Models of Multi-Sided Platforms:
An exploration in the food industry Diane Isabelle, MikaWesterlund, Mohnish Mane and
Seppo Leminen

services, and illustrate the context of our research in
Figure 1.
Studies on DDBM of MSPs in the food industry context
are practically non-existent in spite of several
fundamental changes in consumer behaviours
(different eating patterns, healthy eating trends, rise of
vegan food, preference of ordering in and take-out,
etc.), along with novel offerings and business models
(online ordering, ready-meal kits delivered to offices
and home doors, nutrition-optimized customized
meals, etc.). Therefore, learning from existing solutions
in an industry, either long established or recently
emerged, is an efficient way to contribute to research
on business model innovation (Remane et al., 2017).
Applying our research overview approach (Fig. 1),
which we drew from Lim and colleagues (2018), we first
conducted a literature review of MSPs and DDBMs to
identify key factors involed in data-based value
creation. Following the example of Leminen and
colleagues (2020), we then adopted an illustrative
company cases approach by selecting high growth
digital platform firms in the meal kit and food delivery
industry. We explored their business models and
contrasted their key features with these factors found
in the previous literature. Our goal was to identify key
factors that characterize data-based value creation of
successful DDBMs for MSPs in the food industry. This
research approach is deemed suitable based on
exploratory retrospective intent.
We then proceeded to search for suitable data sources.
Data were collected in 2018 in two stages, using an
archival research method. In the first stage of data
collection, we searched Crunchbase to gather data on
MSP firms in the food industry. Initially, 200
companies were found, using MSPs in the food and
beverage industry as a high level search criteria. We
then further filtered using criteria aligned with the
objectives of our research, that is, successful and high
growth MSPs providing meal kits and food delivery
service that had an established DDBM, and were
operating at the time of the study. We applied the
following criteria from the literature related to firm
survival and high growth: age (over 3 years), customers
(over a million), and annual revenue growth rate (over
50 ). As a result, seven MSPs headquartered in the
U.S. and Europe were chosen as illustrative cases.
Despite a relatively small sample, our criteria and
selection of a specific industry niche allowed us to
identify key attributes of successful DDBMs in that
industry. Other researchers have used similar
approaches given the infancy of the DDBM field (Morris
et al., 2013; Trabucchi et al., 2018).
In the second stage of data collection, we individually
analyzed the selected seven MSPs through
content/archival data analysis to provide accurate
accounts of how they achieved successful DDBMs. Data
used for this purpose were gathered through various
information sources, such as company websites,
industry blogs, app stores offering those companies’
applications, news media, industry journals, and
magazines. News sources included CNBC, Wired,
TechCrunch, Business Insider, VentureBeat, and Business
Times among others.
We gathered and organized the data on each of the
seven cases and conducted content analysis. We were
looking for any indications of whether and how these
MSP firms had adopted data analytics to support and
innovate their DDBMs. We then wrote short case
descriptions of each company focusing on how their
internal and external data were leveraged in their DDBM
and operations. Thereafter, we performed a comparative
analysis to find key (dis)similarities across the cases.
Table 1 summarizes the seven illustrative cases.
Finally, we contrasted the identified key factors related
to the use of data with those identified in the DDBM
literature and classified such factors into DDBM
boosters enabling successful DDBM of MSPs in the food
industry. These factors characterize data-based value
creation resulting in competitive, scalable and profitable
DDBM in that industry.
From our analysis, we identified eight key factors
involved in the role of analytics and data-based value
creation by these successful firms' DDBM. These eight
factors were identified and defined through our
literature review and an in-depth analyses of various
data sources related to our selected MSP cases in the
food industry. In particular, we investigated how the
MSPs leverage their internal and external data, as well as
key performance aspects of their operations. The
resulting factors include market trends, real time
operations, cross-industry affiliation, optimization of
delivery, customer orders, customized
recommendations, customer seasonal demands, and
The Role of Analytics in Data-Driven Business Models of Multi-Sided Platforms:
An exploration in the food industry Diane Isabelle, MikaWesterlund, Mohnish Mane and
Seppo Leminen

business results of media plans (see Table 2 for
definitions). We further classified these factors into
three DDBM boosters: optimization of current services,
prediction of future value, and development of
partnerships, illustrated in Figure 2.
Table 3 summarizes the results of our analysis by
showing which factors appear in each case.
The most widely used data analytics in our cases was
for tracking market trends. For instance, Hello Fresh
makes data-driven decisions by harnessing Google’s
keyword planner to analyze trends in searches at
specific periods of time. The firm also performs data
analyses on dishes that people eat at restaurants.
GrubHub uses data to identify upward trends such as
meals in bowls and vegan dishes. Deliveroo has
established its own business intelligence units in the
Asia Pacific region. Their market trend analyses include
exploring food habits and trends, using advanced
analytics, data science, and local insights. Further, the
company shares its data on customers' preferred dishes
to restaurant partners.
Another important factor is real-time operations.
Deliveroo analyzes and compares the supply of available
delivery drivers with demand based on customer
location. Specifically, they use machine learning
algorithms to compute the most optimal delivery
solution both from the perspective of customers and
delivery drivers. Similarly, Good Eggs uses data to deliver
groceries to their customers in half the time compared to
traditional grocery stores. That said, their real-time
operation factor is more about using dynamically
changing external data such as weather conditions or
traffic data to optimize operations, for example, to
Overview of the illustrative cases
The Role of Analytics in Data-Driven Business Models of Multi-Sided Platforms:
An exploration in the food industry Diane Isabelle, MikaWesterlund, Mohnish Mane and
Seppo Leminen

anticipate the demand for cold drinks on a sunny day.
An example of cross-industry affiliation is the
partnership between Chef'd and Men's Health
Magazine for the purpose of sharing customer data and
gaining mutual access to each other’s customer base.
Chef’d has also partnered with famous chefs to plan
meals and content that appeal to readers. Likewise,
Men's Health Magazine readers can subscribe to Chef'd
meal plans to help achieve their fitness goals. Likewise,
Chef'd customers looking for a healthy lifestyle are
referred to Men's Health Magazine, and receive special
discounts for subscription. Thus, customers from one
side of the platform benefit from services on the other
side.
Optimization of delivery means providing the fastest
delivery service to customers. Both internal and
external data such as customer orders, number of
delivery drivers available, expected time for the meal to
be ready, meal packing time, traffic conditions, and
navigation maps are processed and analyzed to find the
best possible solution to serve customers. Deliveroo uses
Frank, a machine learning algorithm capable of
calculating thousands of operations per second to
provide an optimal delivery solution. This helps them
decrease delivery time and thus also helps delivery
drivers earn more money in tips.
A factor when stressing historical data is customer orders,
which refers to analyzing past customer data
accumulated over a period of time. This generally does
not involve real-time data and does not focus on
customizing offers, but rather on gaining a better
understanding of the customer base and their behaviors.
Historical data can help reveal insightful correlations
that are helpful in modifying the business model. Such
data can include correlations between demographics
Description of identified key factors
The Role of Analytics in Data-Driven Business Models of Multi-Sided Platforms:
An exploration in the food industry Diane Isabelle, MikaWesterlund, Mohnish Mane and
Seppo Leminen

and the type of food that residents in a specific
neighbourhood order. For example, GrubHub
contrasted past customer orders and weather data and
found that their customers preferred mac’n’cheese on
a cold day. Results are then used to modify the
business model to better suit customers and increase
revenue.
Customized recommendations refer to understanding
what the existing customer values. Both current and
historical data are analyzed to find a customer’s
favorite recipes and ingredients. For instance, an
analysis may show that a customer always likes their
sandwich with honey mustard, rather than chili
mayonnaise. In this vein, current and new market
offerings can be personalized according to customers'
preferences, and then suggested for customers to try,
as does Gooble, a small MSP firm offering dinner meals
that can be prepared in 15 minutes with just one pan.
Data analytics is also used to ensure that the MPS’s
offerings are aligned with customers' seasonal demands.
This process involves understanding what food dishes
are popular during a specific season. Based on analytics,
suitable dishes are then created and served to customers
during that time period. This means identifying the
season’s demand through customer data, which can
include, for instance, knowledge about customers’
traditional celebration needs for certain religious
observances or cultural festivals. For example, Hello
Fresh uses data analytics along with knowledge of
holidays like Thanksgiving Day to prepare turkey and
pumpkin-related recipes.
Finally, our cases highlight a factor related to predicting
the impact of media plans. Blue Apronis is affiliated with
a third-party media company that uses predictive
analysis and artificial intelligence to study how well
investments in advertising are paying off. Data analytics
thus serves to provide the firm with an optimum media
mix by providing a forecast of the expected business
results of a media plan or ad campaign, helping Blue
Apron make informed strategic advertising decisions to
achieve cost savings and improve impact.
Comparative business model analysis of illustrative cases
The Role of Analytics in Data-Driven Business Models of Multi-Sided Platforms:
An exploration in the food industry Diane Isabelle, MikaWesterlund, Mohnish Mane and
Seppo Leminen

Our findings highlight that Chef'd, which – despite the
initial growth and success – went out of business in late
2018, only used one of our eight factors. It eventually
ran out of capital before being able to establish a
sustainable and profitable business model in what is
now becoming a competitive industry landscape. We
can see from Table 3 that none of our selected
succesful firms took full advantage of all the
recommended factors.
We classified these eight identified factors into three
distinct DDBM boosters: 1) optimization of current
services, 2) prediction of future value, and 3)
development of partnerships. Real time operations,
optimization of delivery and customized
recommendations. These form the optimization of
current services booster. Market trends, customer
orders, and customer seasonal demand fall under the
prediction of future value booster. Finally, cross
industry affiliation and business results of media plans
fall under the development of partnerships booster.
Figure 2 illustrates the classification of DDBM.
Our objective in this paper was to understand how
digital MSPs in the high-growth food industry,
specifically meal kit and food delivery firms, can leverage
data analytics to establish or adapt their business model
toward a DDBM. In so doing, we aimed to identify key
factors that characterize seven successful DDBMs in that
industry. In summary, we identified eight factors that
reflect the use of data analytics by MSPs in the food
industry, then further classified three DDBM boosters: 1)
optimization of current services, 2) prediction of future
value, and 3) development of partnerships. These
boosters highlight that successful DDBMs are
ambidextrous because they focus simultaneously on the
efficiency of current business and effectiveness of future
business, while also increasing the interdependence in
company value networks. These findings are parallel to
those of Khanagha et al. (2014) who investigated
business model renewal during transition to a cloud
business model. Companies employing these
approaches have been found to be better positioned to
increase sales, improve human resource efficiency,
provide better customer service, reduce marketing costs,
provide optimized delivery service to customers, predict
demand in a more accurate manner, improve value
Classification of key factors and DDBM boosters in the food industry
The Role of Analytics in Data-Driven Business Models of Multi-Sided Platforms:
An exploration in the food industry Diane Isabelle, MikaWesterlund, Mohnish Mane and
Seppo Leminen

propositions, and create new offerings and partnerships.
Our findings are therefore particularly relevant in the
highly competed food industry in which many
companies are currently struggling to create profitable
DDBMs.
Theoretical contributions
The results of the study contribute to the current body of
knowledge on DDBMs in several ways. First, our findings
support the arguments that data analytics, especially
machine learning and artificial intelligence-based
analytics methods, can be deployed both on internal and
external data to achieve cost optimization in online food
delivery. This is a key service across most food MSPs and
one of the fastest growing areas in the industry (Pigatto
et al., 2017). Specifically, analytics can be used to
calculate the optimal delivery solution that takes into
consideration multiple variables, such as the number of
delivery drivers, route traffic, and estimated meal
packing time. Second, our results highlight that data
analytics plays a key role in the DDBMs of food delivery
MSPs in other ways beyond meal delivery. Thus, they
suggest that the capability of conducting big data
analyses and inclusing analytics as a key element of a
company’s business model are necessary to create value
and gain a competitive advantage (Gupta & George,
2016). Third, drawing from previous business model
design and innovation literature (Khanagha et al., 2014;
Zott & Amit, 2020) our study extended the theoretical
concept of “booster”, put forth by Leminen et al.
(forthcoming), suggesting that DDBM boosters can
enable successful data activities in the food industry.
Practical implications
Our findings provide managers in the food industry with
a comprehensive and applicable strategy to develop a
data-driven approach that can be integrated with their
MSP strategy to successfully achieve transformation
toward a DDBM. MSPs operating in the food business
should develop their data analytics capabilities and
adopt continuous data analysis practices on historical
and/or real-time data as a part of their business model,
focusing on the eight key factors identified in this study.
While internal data are relevant to better understand a
company’s customers, there is ample external data
available that can generate value to MSPs with analytics
capabilities. For instance, MSPs can pursue developing
their business toward a DDBM by leveraging seasonal
demand from data analytics.
Further, environmental and cultural factors such as
climate, weather, seasons, festivals, and special
occasions, must be diligently considered. Such data-
driven decisions will help revenue growth. However,
data tends to accumulate, resulting in big data that can
be challenging to manage, especially since much of this
data is unstructured. Compounding this situation, a key
issue is finding skilled labor and developing data
analytics capabilities to use business intelligence
systems. Collaboration within and across industry
sectors can also help in promoting services, while
partnering with a media analytics company can assist
MSPs in predicting the outcomes of their media
advertising costs, using predictive analytics and artificial
intelligence.
Limitations and future research areas
The meal kit and food delivery business area that we
selected for investigation is a rapidly growing yet
relatively new subsection of the food industry.
Therefore, we used an illustrative case approach of
successful MSPs for this study. This enabled us to reach
a better intra-segment generalization of the results. We
further believe that our results and the resulting
classification of DDBM boosters are generalizable to
other MSP industries.
Future research on MSPs in the food industry could
examine a larger sample of companies to gain richer
data and insights on analytics practices, as well as
validate the link between data analytics and the
successes of DDBMs. New entrants have since emerged
in that space, which could exemplify additional DDBM
factors. Testing the applicabilty of our research
approach and performing a case study that could
demonstrate the value of our booster concept in
business model design and innovation are other
potential avenues for investigation. Since studies related
to MSP successes and failures are still largely lacking (de
Reuver et al., 2018), future research could build from our
identified factors, to consider both successes and
failures (Stummer et al., 2018), perhaps using a
longitudinal research perspective and a business model
lifecycle approach (Muzellec et al., 2015). Nonetheless,
we believe that the results illuminate that uses of big
data in food platform businesses will help MSPs develop
more successful DDBMs.
The Role of Analytics in Data-Driven Business Models of Multi-Sided Platforms:
An exploration in the food industry Diane Isabelle, MikaWesterlund, Mohnish Mane and
Seppo Leminen

Amado, A., Cortez, P., Rita, P., & Moro, S. 2018. Research
trends on Big Data in Marketing: A text mining and
topic modeling based literature analysis. European
Research on Management and Business Economics,
24(1): 1-7.
https://doi.org/10.1016/j.iedeen.2017.06.002
Andersen, T.C.K., & Bjerrum, T.C.B. 2016. Service and
data driven multi business model platform in a world
of persuasive technologies. Journal of Multi Business
Model Innovation and Technology, 4(1): 47-60.
https://doi.org/10.13052/jmbmit2245-456X.413
Baldassarre, B., Calabretta, G., Bocken, N.M.P., &
Jaskiewicz, T. 2017. Bridging sustainable business
model innovation and user-driven innovation: A
process for sustainable value proposition design.
Journal of Cleaner Production, 147: 175-186.
https://doi.org/10.1016/j.jclepro.2017.01.081
Balducci, B., & Marinova, D. 2018. Unstructured data in
marketing. Journal of the Academy of Marketing
Science, 46(4): 557-590.
https://doi.org/10.1007/s11747-018-0581-x
Biglaiser, G. 1993) Middlemen as experts. The RAND
journal of Economics, 212-223.
https://www.jstor.org/stable/2555758
Blazquez, D., & Domenech, J. 2018. Big Data sources
and methods for social and economic analyses.
Technological Forecasting and Social Change, 130: 99-
113.
https://doi.org/10.1016/j.techfore.2017.07.027
Brownlow, J., Zaki, M., Neely, A., & Urmetzer, F. 2015.
Data and analytics-data-driven business models: A
Blueprint for Innovation. Cambridge Service Alliance.
Clarke, R. 2016. Big data, big risks. Information Systems
Journal, 26(1): 77-90.
https://doi.org/10.1111/isj.12088
De Mauro, A., Greco, M., & Grimaldi, M. 2016. A formal
definition of Big Data based on its essential features.
Library Review, 65(3): 122-135.
https://doi.org/10.1108/LR-06-2015-0061
de Oliveira, D.T., & Cortimiglia, M.N. 2017. Value co-
creation in web-based multisided platforms: A
conceptual framework and implications for business
model design. Business Horizons, 60(6): 747-758.
https://doi.org/10.1016/j.bushor.2017.07.002
de Reuver, M., Sørensen, C., & Basole, R.C. 2018. The
digital platform: a research agenda. Journal of
Information Technology, 33(2): 124-135.
https://doi.org/10.1057/s41265-016-0033-3
Dubé, L., Du, P., McRae, C., Sharma, N., Jayaraman, S.,
& Nie, J.- Y. 2018. Convergent Innovation in Food
through Big Data and Artificial Intelligence for
Societal-Scale Inclusive Growth. Technology
Innovation Management Review, 8(2): 49-65.
http://doi.org/10.22215/timreview/1139
Engelbrecht, A., Gerlach, J., & Widjaja, T. 2016.
Understanding the anatomy of data-driven business
models-towards an empirical taxonomy. In ECIS,
Research Paper 128.
Erevelles, S., Fukawa, N., & Swayne, L. 2016. Big Data
consumer analytics and the transformation of
marketing. Journal of Business Research, 69(2): 897-
904.
https://doi.org/10.1016/j.jbusres.2015.07.001
Evans, D. 2003. The antitrust economics of multi-sided
platform markets. Yale Journal on Regulation, 20(2):
325-381.
https://digitalcommons.law.yale.edu/yjreg/vol20/iss
2/4
Fiorini, P. de C., Seles, B.M.R.P., Jabbour, C.J.C.,
Mariano, E.B., & Jabbour, A.B.L. de S. 2018.
Management theory and big data literature: From a
review to a research agenda. International Journal of
Information Management, 43: 112-129.
https://doi.org/10.1016/j.ijinfomgt.2018.07.005
Ghasemaghaei, M. 2018. Improving organizational
performance through the use of big data. Journal of
Computer Information Systems.
https://doi.org/10.1080/08874417.2018.1496805
Gupta, M., & George, J. F. 2016. Toward the
development of a big data analytics capability.
Information & Management, 53(8): 1049-1064.
https://doi.org/10.1016/j.im.2016.07.004
Hagiu, A. 2006. Pricing and Commitment by Two-Sided
Platforms. Rand Journal of Economics, 37(3): 720-237.
https://doi.org/10.1111/j.1756-2171.2006.tb00039.x
Hagiu, A., & Wright, J. 2015. Multi-sided platforms.
International Journal of Industrial Organization, 43:
162-174.
https://doi.org/10.1016/j.ijindorg.2015.03.003
Hartmann, P. M., Zaki, M., Feldmann, N., & Neely, A.
2016. Capturing value from big data–a taxonomy of
data-driven business models used by start-up firms.
International Journal of Operations & Production
Management, 36(10): 1382-1406.
https://doi.org/10.1108/IJOPM-02-2014-0098
Hmoud, A.Y., Salim, J., & Yaakub, M.R. 2017. A two-
sided market mechanisms toward designing a big
data-driven business model for Mobile Network
Operators (MNOs). Journal of Telecommunication,
Electronic and Computer Engineering (JTEC), 9(2-9):
105-110.
Ikeda, K., & Marshall, A. 2019. Strategies for competing
in markets enabled by digital platforms. Strategy &
Leadership, 47(1): 30-36.
https://doi.org/10.1108/SL-10-2018-0097
Jones, M. 2019. What we talk about when we talk about
(big) data. Journal of Strategic Information Systems,
28(1): 3-16.
https://doi.org/10.1016/j.jsis.2018.10.005
The Role of Analytics in Data-Driven Business Models of Multi-Sided Platforms:
An exploration in the food industry Diane Isabelle, MikaWesterlund, Mohnish Mane and
Seppo Leminen

Katz, M., & Shapiro, C. 1985. Network externalities,
competition, and compatibility. American Economic
Review, 75(3): 424-440.
https://www.jstor.org/stable/1814809
Khanagha, S., Volberda, H., & Oshri, I. 2014. Business
model renewal and ambidexterity: structural
alteration and strategy formation process during
transition to a Cloud business model. R&D
Management, 44(3): 322-340.
https://doi.org/10.1111/radm.12070
Koskinen, J. 2018. How to Build Competencies for a
Data-Driven Business: Keys for Success and Seeds for
Failure. Technology Innovation Management Review,
8(10): 18-28.
http://doi.org/10.22215/timreview/1190
Ladd, B. 2018. Demise of Chef'd points to stark choice
for meal-kit companies: Get acquired, or die. Forbes.
Retrieved from
https://www.forbes.com/sites/brittainladd/2018/07/
18/the-meal-kit-company-chefd-is-no-more-what-
does-it-mean-for-the-meal-kit-
industry/#5da565e111bf
Leminen, S., Nyström, A.-G., & Westerlund, M.
(forthcoming). Change processes in open innovation
networks - exploring living labs. Industrial Marketing
Management.
https://doi.org/10.1016/j.indmarman.2019.01.013
Leminen, S., Rajahonka, M., Wendelin, R., &
Westerlund, M. 2020. Industrial internet of things
business models in the machine-to-machine context.
Industrial Marketing Management, 84: 298-311.
https://doi.org/10.1016/j.indmarman.2019.08.008
Leventhal, R. 2013. Trend: Big data analytics: From
volume to value. Healthcare Informatics, 30(2): 12-14.
Lim, C., Kim, K.H., Kim, M.J., Heo, J.Y., Kim, K.J., &
Maglio, P.P. 2018. From data to value: A nine-factor
framework for data-based value creation in
information-intensive services. International Journal
of Information Management, 39: 121-135.
https://doi.org/10.1016/j.ijinfomgt.2017.12.007
Marshall, A., Mueck, S., & Shockley, R. 2015. How
leading organizations use big data and analytics to
innovate. Strategy & Leadership, 43(5): 32-39.
https://doi.org/10.1108/SL-06-2015-0054
Mathis, K., & Köbler, F. 2016. Data-Need Fit – Towards
data-driven business model innovation. Proceedings
from the ServDes 2016 - Fifth Service Design and
Innovation conference: 458-467.
McAfee, A., & Brynjolfsson, E. 2012. Big data: The
management revolution. Harvard Business Review,
90: 61-69.
Morabito, V. 2015. Big Data and Analytics: Strategic and
Organizational Impacts. Berlin: Springer
International Publishing.
Moro Visconti, R., Larocca, A., & Marconi, M. 2017. Big
Data-Driven value chains and digital platforms: from
Value Co-Creation to Monetization. Available at:
SSRN 2903799.
Morris, M.H., Shirokova, G., & Shatalov, A. 2013. The
business model and firm performance: The case of
Russian food service ventures. Journal of Small
Business Management, 51(1): 46-65.
https://doi.org/10.1111/j.1540-627X.2012.00377.x
Muzellec, L., Ronteau, S., & Lambkina, M. 2015. Two-
sided Internet platforms: A business model lifecycle
perspective. Industrial Marketing Management, 45:
139-150.
https://doi.org/10.1016/j.indmarman.2015.02.012
Pigatto, G., Machado, J.G.D.C.F., Negreti, A.D.S. &
Machado, L.M. 2017. Have you chosen your request?
Analysis of online food delivery companies in Brazil.
British Food Journal, 119(3): 639-657.
https://doi.org/10.1108/BFJ-05-2016-0207
Porter, M.E. & Kramer, M.R. 2011. The big idea: Creating
shared value. Harvard Business Review, 89(1), 2.
Redman, T.C. 2015. 4 Business models for the data age.
Harvard Business Review, 93(5).
Remane, G., Hanelt, A., Tesch, J.F., & Kolbe, L.M. 2017.
The business model pattern database — A tool for
systematic business model innovation. International
Journal of Innovation Management, 21(1): 1750004.
https://doi.org/10.1007/978-3-319-98723-1_5
Rochet, J.C., & Tirole, J. 2006. Two-sided markets: A
progress report. RAND Journal of Economics, 37(3):
645-667.
https://www.jstor.org/stable/25046265
Sorescu, A. 2017. Data driven business model
innovation. Journal of Product Innovation
Management, 34(5): 691-696.
https://doi.org/10.1111/jpim.12398
Sun, Z., & Huo, Y. 2019. The Spectrum of Big Data
Analytics. Journal of Computer Information Systems.
https://doi.org/10.1080/08874417.2019.1571456
Stummer, C., Kundisch, D., & Decker, R. 2018. Platform
launch strategies. Business & Information Systems
Engineering, 60(2): 167-173.
https://doi.org/10.1007/s12599-018-0520-x
Trabucchi, D., Buganza, T., & Pellizzoni, E. 2017. Give
Away Your Digital Services. Research-Technology
Management, 60(2): 43-52.
https://doi.org/10.1080/08956308.2017.1276390
Vidgen, R., Shaw, S., & Grant, D.B. 2017. Management
challenges in creating value from business analytics.
European Journal of Operational Research, 261(2):
626-639.
https://doi.org/10.1016/j.ejor.2017.02.023
Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J.F.,
Dubey, R. & Childe, S.J. 2017. Big data analytics and
firm performance: Effects of dynamic capabilities.
Journal of Business Research, 70: 356-365.
https://doi.org/10.1016/j.jbusres.2016.08.009
Wang, Y., Kung, L., Ting, C., & Byrd, T. A. 2015. Beyond a
technical perspective: understanding big data
capabilities in health care. In System sciences (HICSS),
48th Hawaii international conference: 3044-3053.
The Role of Analytics in Data-Driven Business Models of Multi-Sided Platforms:
An exploration in the food industry Diane Isabelle, MikaWesterlund, Mohnish Mane and
Seppo Leminen

Diane Isabelle is an Associate Professor of
International Business. Her research focuses
broadly on the areas of science, innovation and
techno-entrepreneurship within a global context.
Specifically, her research is organized around the
following three inter-related themes: 1)
International entrepreneurship & ecosystems, 2)
Internationalization (International New Ventures
and SMEs), 3) Global collaborative research and
Science, Technology and Innovation policy. In
addition to these themes, she is researching and
publishing on Technology-integrated and
international interdisciplinary experiential learning
in higher education. Prior to joining Sprott in 2011,
Dr. Isabelle worked in several senior executive roles
related to science, technology and industrial
research (Industrial Research Assistance Program -
IRAP) at the National Research Council of Canada
(NRC), the Government of Canada’s premier
research and technology organization. She started
her career as a project engineer for several
multinational firms, including General Electric,
Esso and Boeing Aerospace.
Mika Westerlund, DSc (Econ), is an Associate
Professor at Carleton University in Ottawa, Canada.
He previously held positions as a Postdoctoral
Scholar in the Haas School of Business at the
University of California Berkeley and in the School
of Economics at Aalto University in Helsinki,
Finland. Mika earned his doctoral degree in
Marketing from the Helsinki School of Economics
in Finland. His research interests include open and
user innovation, the Internet of Things, business
Citation: Isabelle, D., Westerlund, M., Leminen, S. 2020. The Role of
Analytics in Data-Driven Business Models of Multi-Sided Platforms:
An exploration in the food industry. Technology Innovation
Management Review, 10(7): 4-15.
http://doi.org/10.22215/timreview/1371
Keywords: Digital platforms, data analytics, data-driven business
models DDBM boosters, food industry
White, M. 2012. Digital workplaces: Vision and reality.
Business Information Review, 29: 205–214.
https://doi.org/10.1177/0266382112470412
Wong, R. 2012. Big data privacy. Journal of Information
Technology Software Engineering, 2(5): 114.
Zaki, M., Lillegraven, T., & Neely, A. 2015. Moving
Towards a Data-Driven Business Model (DDBM) in
the Online Newspaper Publishing Industry.
University of Cambridge.
Zott, C., & Amit, R. 2010. Business model design: an
activity system perspective. Long range planning,
43(2-3): 216-226.
https://doi.org/10.1016/j.lrp.2009.07.004
The Role of Analytics in Data-Driven Business Models of Multi-Sided Platforms:
An exploration in the food industry Diane Isabelle, MikaWesterlund, Mohnish Mane and
Seppo Leminen
strategy, and management models in high-tech
and service-intensive industries.
Mohnish Mane, MEng, is a Senior Business Analyst
at NTT Data Canada. Previously, he held a similar
position at Tata Consultancy services. Mohnish
earned his Master’s degree in Technology
Innovation Management at Carleton University,
focussing on data driven business models. He is a
solutions-driven business analyst with diverse
experience in Power, Healthcare and Oil and Gas
industries where he has lead cross functional teams
in the development, documentation and delivery of
complex IT projects. In his free time, he is involved
in conducting various cooperate social
responsibility events and volunteering
opportunities.
Seppo Leminen is a Full Professor of Innovation
and Entrepreneurship in the USN School of
Business at the University of South-Eastern Norway
in Norway, a Research Director at Pellervo
Economic Research in Finland, an Adjunct
Professor of Business Development at Aalto
University in Finland and an Adjunct Research
Professor at Carleton University in Canada. He
holds a doctoral degree in Marketing from the
Hanken School of Economics and a doctoral degree
in Industrial Engineering and Management in the
School of Science at Aalto University. His research
and consulting interests include living labs, open
innovation, innovation ecosystems, robotics, the
Internet of Things (IoT), as well as management
models in high-tech and service-intensive
industries. Results from his research have been
reported in Industrial Marketing Management, the
Journal of Cleaner Production, the Journal of
Engineering and Technology Management, the
Journal of Business & Industrial Marketing,
Management Decision, the International Journal of
Innovation Management, and the Technology
Innovation Management Review, among many
others.