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The Internet of Everything: Smart things and their impact on business models

January 2020
Journal of Business Research 122

January 2020
122

DOI:10.1016/j.jbusres.2019.12.035

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Authors:

Irene C L Ng

The University of Warwick

Stefan Stieglitz

Universität Potsdam

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The internet of everything (IoE), connecting people, organizations and smart things, promises to fundamentally change how we live, work and interact, and it may redefine a wide range of industry sectors. This conceptual paper aims to develop a vision of how the IoE may alter business models and the ways in which individuals and organizations create value. We review literature on networked business models and service ecosystems, and show that a clearer understanding is needed of how the IoE will impact on the ways that organizations go about their business at the micro, meso and macro levels. Combining this with an inductive, vignette-based approach, we present a new taxonomy of smart things based on their capabilities and their connectivity. We derive their implications for business models and conclude the paper with propositions that form a research agenda for business researchers.

Taxonomy of the increasing levels of smartness for things in the Internet of Everything, (A) their implications for business models (BMs), and (B) the abstraction level of their effects in the economy.

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Contents lists available at ScienceDirect

Journal of Business Research

journal homepage: www.elsevier.com/locate/jbusres

The Internet of Everything: Smart things and their impact on business

models

David J. Langley

a,b,⁎

, Jenny van Doorn

, Irene C.L. Ng

, Stefan Stieglitz

, Alexander Lazovik

Albert Boonstra

TNO, Netherlands Organisation for Applied Scientiﬁc Research, Netherlands

University of Groningen, Netherlands

University of Warwick, United Kingdom

University of Duisburg-Essen, Germany

ARTICLE INFO

Keywords:

Digitalization

Artiﬁcial intelligence

Networked business models

Service-dominant logic

Value creation

ABSTRACT

The internet of everything (IoE), connecting people, organizations and smart things, promises to fundamentally

change how we live, work and interact, and it may redeﬁne a wide range of industry sectors. This conceptual

paper aims to develop a vision of how the IoE may alter business models and the ways in which individuals and

organizations create value. We review literature on networked business models and service ecosystems, and

show that a clearer understanding is needed of how the IoE will impact on the ways that organizations go about

their business at the micro, meso and macro levels. Combining this with an inductive, vignette-based approach,

we present a new taxonomy of smart things based on their capabilities and their connectivity. We derive their

implications for business models and conclude the paper with propositions that form a research agenda for

business researchers.

1. Introduction

The rise of the Internet of Things (IoT), has the potential to change

the world as we know it and will fundamentally change many compa-

nies’business models as well as the way consumers interact with these

companies and other stakeholders (Fredette, Marom, Steinert, &

Witters, 2012). Over the past ﬁve years, common day-to-day objects

have evolved to embed new capabilities, often through connectivity

linking sensors and control systems. From door bells, cars, fridges, and

TVs, to things where the beneﬁts of connectivity are less immediately

obvious such as ice cubes and clothing, the IoT is slowly creeping into

the daily lives of citizens and businesses (Ng, Scharf, Pogrebna, &

Maull, 2015). The term IoT is being used to describe the connectivity of

things as “a system of uniquely identiﬁable and connected constituents

(termed as Internet-connected constituents) capable of virtual representation

and virtual accessibility leading to an Internet-like structure for remote lo-

cating, sensing, and/or operating the constituents with real-time data/in-

formation ﬂows between them”(Ng & Wakenshaw, 2017, p. 6). For the

purposes of this paper, we use the term “smart things”to describe these

connected constituents.

The explosion of connectivity is subtle and often not noticeable to

many people. Hyperconnectivity as a “myriad means of communication

and interaction”that is always on, readily accessible, information-rich

and interactive enables connections between virtually everything, re-

sulting in the broadening of the IoT concept to the Internet of

Everything (Fredette et al., 2012). The Internet of Everything expands

the IoT concept by adding links to data, people and (business) pro-

cesses. It therefore comprises other connection-based paradigms such as

IoT, Internet of People (IoP), and Industrial Internet (II) (Yang, Di

Martino, & Zhang, 2017). In this context we understand the Internet of

Everything (IoE) as a network of connections between smart things, people,

processes, and data with real-time data/information ﬂows between them.

Despite the huge interest in these new concepts that have the po-

tential to radically alter where we live, how we work and how we in-

teract with each other and with organizations (Fredette et al., 2012),

there is a lack of understanding of how the emergence of the IoE will

impact businesses. Businesses that succeed in adapting their extant

business models to the new technological possibilities have consider-

able opportunities to innovate and are potentially highly competitive.

Yet, the IoE also poses considerable challenges to ﬁrms, including the

development of interoperability between systems, coping with en-

trenched industry partners that do not collaborate with the new de-

velopments, path-dependent legacy processes and transactions, con-

tractual and liability issues, security challenges, loss of control, as well

https://doi.org/10.1016/j.jbusres.2019.12.035

Received 29 June 2018; Received in revised form 19 December 2019; Accepted 20 December 2019

⁎

Corresponding author at: TNO, Eemsgolaan 3, 9727DW Groningen, Netherlands.

E-mail address: david.langley@tno.nl (D.J. Langley).

Journal of Business Research xxx (xxxx) xxx–xxx

(http://creativecommons.org/licenses/BY/4.0/).

Please cite this article as: David J. Langley, et al., Journal of Business Research, https://doi.org/10.1016/j.jbusres.2019.12.035

as privacy concerns related to the explosion of data collected and used

by businesses and their smart things. For businesses, it is therefore

important to understand the extent to which smart things will transform

existing business models, and as a part of this, how value creation in

such service ecosystems will be aﬀected by the rise of the IoE. This

would critically depend on the conﬁguration of the smart things and

their capabilities.

In this paper, we ﬁrst use current theoretical expositions on net-

worked business models and service-based value-creating ecosystems to

describe the potential impact of IoE on current business models and the

processes of value creation by ﬁrms and their customers. We then

propose a conceptual taxonomy of diﬀerent levels of smartness that

things can be endowed with. Finally, using this taxonomy, we explore

the impact of diﬀerent levels of smartness on business models and

evolve a research agenda. Our study proposes that the connectivity

impact of the IoE goes beyond the boundaries of a single company and

its business model, thus requiring a multi-layered understanding. We

discuss potential implications of the IoE by framing it through micro,

meso, and macro levels of a service ecosystem (Ng & Vargo, 2018; Ng &

Wakenshaw, 2018). As part of the research agenda, we develop theory-

based propositions for future research that are important from both a

scientiﬁc and a management perspective.

In the next sections we discuss extant literature on networked

business models and the service-dominant logic in relation to how

smart things may impact business models, and we explore the impact of

these changes on the process of value creation. Following that, and

acknowledging that the impact of smart things on business models may

critically depend on their conﬁguration, we develop a taxonomy where

we describe conceptually diﬀerent levels of smartness that things can be

endowed with. In the discussion, we draw on this analysis and insights

from the two literature streams, and we derive propositions showing

avenues for future business research.

1.1. Methodology

This research is conceptual in nature and takes a multidisciplinary

approach in investigating the phenomenon of endowing things with

smartness, and its consequences. Owing to the diverse, recency and

transcendental nature of the topic, a critical literature review on the IoE

is a challenge, compounded by the scarcity of relevant papers published

in scientiﬁc management journals. Therefore, we chose two theoretical

lenses most relevant to the breadth of the challenge and use them to

frame the phenomenon of smart things and how they impact on busi-

ness models: technology-enabled networked business models and value-

creating service ecosystems. Additionally, we consider the technologies

that are forming the IoE and what their implications are for enabling

smartness. By combining these perspectives, we are able to provide a

new comprehensive and multi-faceted exposition of how smart things

impact on business models.

We combine a deductive with an inductive approach. On the one

hand, we build our understanding of the implications of increasing le-

vels of smartness for business models based on the two theoretical

lenses mentioned above. On the other, our research follows an in-

ductive approach through a vignette description of real examples, to

both interrogate and expand on the theoretical foundations, as well as

develop our understanding of smart things in the IoE. We apply this

latter approach particularly when current theoretical insights appear

inadequate to explain the phenomenon at hand. Subsequently, we de-

velop a new taxonomy to make explicit what levels of smartness of

things in the IoE are, and what their implications are for business

models. Finally, we derive propositions for future research, not only for

theory testing, but also for the development of novel theoretical con-

cepts.

2. Theoretical lenses for the Internet of Everything

While many disciplines have touched on the subject of smart tech-

nologies and their inﬂuence on the way that businesses operate from

their speciﬁc point of view, there appears to be a lack of comprehensive

approaches that explain the phenomenon and its consequences for

business models. Therefore, we base our theoretical discussion on two

complementary streams of literature, since combining lenses brings

major beneﬁts in terms of new insights and novel hypotheses

(Okhuysen & Bonardi, 2011). We look to the ongoing discussion in the

management and information systems journals regarding technology-

enabled business models, including digital business models, which go

beyond the boundaries of a single organization. We also consider the

growing literature on value creation in service ecosystems through a ser-

vice-dominant logic approach, which reﬂects the growing appreciation of

the role of the end users of products and services as co-creators of value.

These perspectives enable us to emerge the importance of an extra-or-

ganizational ecosystem level in the developing theory on technologies'

inﬂuence on business.

2.1. Technology-enabled networked business models

Despite serious concerns about the business model concept’s con-

struct validity and explanatory power (Doganova & Eyquem-Renault,

2009; Foss & Saebi, 2017; Massa, Tucci, & Afuah, 2017), it has been

developed and applied in multiple ways both scientiﬁcally and in

practice. In the ﬁeld of Technology and Innovation Management, a key

direction has been the new opportunities for creating value oﬀered by

digital technologies, such as those incorporated in the IoE (Gambardella

& McGahan, 2010; Keen & Williams, 2013; Wirtz, Schilke, & Ullrich,

2010). Indeed, digital technology is not simply “yet another tech-

nology”in this respect; it oﬀers opportunities throughout the entire

process of value creation and appropriation whereby it inﬂuences not

only the functional level of business operations but also the strategic

level of business purpose and ability to generate new value propositions

(Barrett, Davidson, Prabhu, & Vargo, 2015; Massa et al., 2017). Fol-

lowing Massa et al. (2017),wedeﬁne business models as attributes of

ﬁrms in terms of the activities they perform, and the outcomes they generate,

that determine their performance in markets.

In their review of relevant literature, Foss and Saebi (2017) identify

the need for more predictive and theory-advancing research into digital

technologies as an antecedent to business model innovation. A key

motivation is the role of network externalities that are inherent in the

increasing number of internet-based products and services; these play

an important role in deﬁning the value of a ﬁrm’soﬀerings, as potential

customers’perceptions are strongly inﬂuenced by expectations of the

“winner takes all”market scenarios (Cennamo & Santalo, 2013;

Loebbecke & Picot, 2015).

One group of studies highlight the beneﬁttoﬁrms that are able to

employ the dynamic capabilities necessary to take advantage of smart

things (Teece, 2012). Within organizations, smart things may comple-

ment the capabilities of employees thereby enhancing the overall

ability of the ﬁrm to eﬃciently and eﬀectively operate, particularly if

the smart technology helps people to free up time for the tasks they are

best able to carry out (Marinova, de Ruyter, Huang, Meuter, &

Challagalla, 2017). Other studies highlight how smart things may

endow ﬁrms with new abilities for ﬂexible adaptation, explaining how

organizations rapidly adapt to changing circumstances in order to proﬁt

from new opportunities or avoid damaging threats (Drnevich & Croson,

2013; Teece, Pisano, & Shuen, 1997). These scholars posit that the type

of digital data available in the IoE oﬀers rich new opportunities for

enhancing this ﬂexibility, whereby a “superadditive”eﬀect can boost

the data's value when it is used by organizations with a high level of

ﬂexibility (Drnevich & Croson, 2013); having more data makes having a

given amount of ﬂexibility more valuable, and vice versa.

However, to what extent these potential advantages can be realized

D.J. Langley, et al. Journal of Business Research xxx (xxxx) xxx–xxx

is dependent on just how smart the technology functions; this means

that diﬀerent levels of smartness may exist. If the technology is not very

smart yet, employees have to expend signiﬁcant time in data prepara-

tion and training of the smart thing, such that people are tethered to the

supposed smart thing as a form of human assistant (Huang & Rust,

2018). Only when the things achieve a higher level of smartness, such

that they can deal with messy, heterogeneous data, will the people be

free to fruitfully handle their speciﬁc tasks, such as interpretation and

ideation. Indeed, smart things pose many new challenges to the ﬁrm,

not least the diﬃculties of adapting existing, and often historically

successful, business models to new digital possibilities. When ﬁrms fail

to develop their business models in this way, they may quickly ﬁnd

themselves at a disadvantage compared to their digitally savvy com-

petitors (Benner & Tripsas, 2012; Massa et al., 2017).

In the context of the IoE, an important and highly relevant devel-

opment in business model thinking is the notion of an abstraction level

that is higher than one single ﬁrm. Network-level business models are

becoming increasingly more relevant than the ﬁrm-centric level alone

(Adner, 2017; Bankvall, Dubois, & Lind, 2017; Oskam, Bossink, & de

Man, 2018), and network-focused studies are increasingly acknowl-

edging how a ﬁrm’s activities are interdependent with its partners

whereby the process of value creation is boundary-spanning (Zott &

Amit, 2010; Zott, Amit, & Massa, 2011). Through the IoE, networks of

ﬁrms, people and things will overlap in more tightly coupled systems,

such that the connections between these entities will challenge the

existing structures of industry and markets, and in turn ﬁrms’business

models (Turber, Vom Brocke, Gassmann, & Fleisch, 2014; Westerlund,

Leminen, & Rajahonka, 2014). When information can ﬂow from a door

to a phone, markets that were never connected become ‘wet-wired’,

much like a short circuit, resulting in the value proposition of a ﬁrm

within one industry being realized in conjunction with another ﬁrm

from a diﬀerent industry. This leads to the potential fracturing of

business models in individual industries (Ng, 2014).

Thus, digital technology inherently lends itself to cross-sectoral in-

novations, challenging ﬁrms from previously unconnected industries to

work together. It removes traditional entry barriers to existing markets,

and intensiﬁes competition. Together, these challenges are forcing ﬁrms

to redesign their value propositions and their long-term business stra-

tegies for shareholder value, proﬁt and growth (Barrett et al., 2015;

Majchrzak, Markus, & Wareham, 2016).

If we attempt to assess the extent to which this literature can be

applied to smart products and things, then we may conclude that theory

development for network-embedded business models is needed

(Bankvall et al., 2017). There is a paucity of theoretical argumentation,

especially with respect to an explanation of the consequences of smart

things for business models. We may criticize extant literature for failing

to address the eﬀects of pervasive connectivity on ﬁrms’business

strategies. The levels of connectivity being brought about by the IoE

does not simply oﬀer connection to social networks and applications,

but enables wholly new forms of ‘smartness’embedded in network

constituents due to the interconnection between data streams from both

social and physical systems and sources. A major challenge associated

with such a shift is how ﬁrms develop their existing business model

towards a new networked business model that ﬁts best in the IoE

context. This implies that the development of a new business model has

to deal with path dependency eﬀects as the organization attempts to

extend its current strategy and value propositions step-by-step in the

direction of the new IoE context (Wirtz et al., 2010). Any new theo-

rizing must be able to guide the creation of new business models

starting from the context of smart things at a low smartness level right

up to fully autonomous, adaptive and self-determining things.

2.2. Service ecosystems

Theorizing business models in the age of the IoE requires a mindset

that is less linear, less cause-and-eﬀect inclined, less dyadic (customer

and ﬁrm) in nature and less sequential in its process. In other words,

when objects and people are interconnected, resource ﬂows may not

always be deterministic, or follow the original design. Business models

of one industry overlap with others, resulting in unintended or un-

foreseen consequences.

A potentially informative way of understanding business models in

a hyperconnected world is to adopt a service ecosystems mindset (Ng &

Wakenshaw, 2018). This ecosystem view, developed from service-

dominant logic (S-D logic), can provide a framework for studying wider

systems, or the interaction and value co-creation among multiple ser-

vice systems (Vargo & Lusch, 2017). S-D logic as a perspective for

service has been extensively discussed (Vargo & Lusch, 2004; 2016;

2017; Vargo & Akaka, 2012), although less has been written on the

business models that might emerge once connectivity and interrelations

increase dramatically. Its fundamental premises have been explained at

length (Vargo & Lusch, 2004, 2017; Vargo & Akaka, 2012), and the S-D

logic’s basic tenet is that service –the application of competences for

the beneﬁts of another –is the basis of all exchange which means that

all economies can therefore be understood to be service economies.

Service-for-service exchange is recognised as a theoretical foundation

for the development of service science and the study of service systems

(Maglio & Spohrer, 2008; Vargo & Akaka, 2012). Under this logic, value

is co-created by multiple actors, each of which integrates their own

competences and resources with those of the other actors; a concept

that can also be extended to objects as actors in a hyperconnected

world.

Service ecosystems in IoE is a useful framework as it sets out why

and when a system is a service ecosystem, which is when the ﬂow be-

tween actors is that which results in mutual value creation, through

actors’service-for-service exchanges (Ng & Wakenshaw, 2018). An es-

sential aspect of service ecosystems is the coordination mechanisms

through which actors are able to co-create value and enact resource-

integrating practices. Such practices occur within institutions, i.e.

through norms that are both explicit and implicit (Furubotn & Richter,

2005; North, 1990), with certain enforcement guarantees that could be

formal or informal. Hence, institutions create expectations regarding

the behavior of actors within a value-creating and resource-integrating

service ecosystem and can be enabling or disabling. According to the S-

D logic, a particular institutional arrangement is a set of rules and values

that drive a set of behaviors (Vargo & Lusch, 2016). More importantly,

institutional arrangements enable coordination between actors of a

system and have regulative, normative and cognitive functions in

creating value (Kleinaltenkamp, 2018).

When people, things and businesses become connected in IoE, they

are confronted with institutional arrangements that may be mutually

incompatible. Conﬂicting institutional arrangements bring about com-

plexity (Greenwood, Raynard, Kodeih, Micelotta, & Lounsbury, 2011),

resulting intensions within and across organizations. Thus IoE has the

potential to disrupt how entrenched actors conform to speciﬁc institu-

tional arrangements and when such disruptions occur, a system may

exhibit uncertainty and conﬂicts.

2.3. A combined perspective

When we combine the perspectives of service ecosystems and the

technology-enabled business models to the IoE, the advancement of the

technology seems key to the understanding of its consequences. “Flows”

of resources from one actor to the other can also comprise ﬂows of

information between diﬀerent smart things, or other entities connected

through the IoE. The extent to which smart things are connected will

largely determine the ﬂows of information and therewith the reach and

richness of the service ecosystem.

Technology-enabled networks of people and things are value co-

creating actors actively involved in value generation and appropriation

via the application of their competencies. Combining the use of tech-

nology-enabled business models with a service ecosystems perspective

D.J. Langley, et al. Journal of Business Research xxx (xxxx) xxx–xxx

in the S-D logic is a useful frame for IoE business models. It presents an

opportunity to ‘zoom out’and broaden the perspective (Vargo & Lusch,

2016). More importantly, these combined views can provide a multi-

layered understanding of IoE, from micro to meso to macro levels, so

that the impact of a business model at a micro level can be better un-

derstood in terms of its inﬂuence by events at the meso level; the wider

economic or market level. Such a frame also provides an understanding

of interactions between diﬀerent business models at the meso level,

such as when smart things, which may be sold by a focal ﬁrm, connect

with other smart things, sold by other ﬁrms with diﬀerent business

models. When many such interacting business models are considered

together, they provide an understanding at the macro level, such as that

of a regional economy, or an industry sector.

Although these two theoretical lenses diﬀer in focus; the ﬁrst cen-

tered on technology-enabled business models and the second on service

ecosystems, we consider their conceptual distance as low and the

compatibility of their conceptual assumptions as high (Okhuysen &

Bonardi, 2011). Both lenses emerged within management disciplines

(Information Systems and Marketing respectively) and focus on value

creation through an analysis of the entire process. While most literature

in business models traditionally focuses on the ﬁrm’s business model as

a unit of analysis without a framework for understanding how other

ﬁrms’business models within related industries might normatively

impact the ﬁrm, more recent literature goes beyond the boundaries of a

single organization. For example, embedding a sensor into a door al-

lows for a connection between the business model of a door manu-

facturer with that of actors in the security industry. A service ecosystem

worldview, as suggested by these two theoretical lenses that describe

value creation occurring across networked systems, through the un-

derstanding of resource integration at micro, meso and macro levels can

provide insights into this new phenomenon and, as such, bring insights

into how the IoE could change business models.

Summing up, the IoE has the potential to radically alter current

business models and the conﬁguration of existing business networks,

where the combined perspectives of the technology-enabled business

models and the service ecosystems view is a useful and appropriate

theoretical lens to conceptualize these changes. The extent to which

networked business models will evolve to cross domains and industries

depends on how advanced IoE technologies become, most notably the

level of smartness that objects possess. However, there is a lack of

theory on this important characteristic of future business models em-

bedded in the IoE. In the following section, we therefore develop a

taxonomy for diﬀerent levels of the smartness of things.

3. Enabling technologies for the Internet of Everything

Technology is clearly a major driver for the increasing smartness of

things in the IoE. We highlight four areas of technical development and

specify how they relate to the notion of smartness: interconnectivity,

big data (Xu, He, & Li, 2014), artiﬁcial intelligence (Russell & Norvig,

2009), and semantic interoperability (Miorandi, Sicari, De Pellegrini, &

Chlamtac, 2012).

First, the emergence of IoT as a global interconnected network of

things has opened up new opportunities for increasing the level of

smartness of things. One of the key aspects for smart things is their

ability to sense the environment to understand the state of the system in

order to act accordingly. Interconnectivity allows for an increase in the

specialization of smart components, as each component can then focus

on one particular aspect (such as sensing) while other interconnected

components can exploit that functionality to use and improve its own

performance. This specialization in the constituents of the IoE is useful

when each constituent is reactive to the environment and to other

constituents to which it is connected. In relation to the IoE there are

now new challenges for the supporting capabilities collectively termed

function-as-a-service, often provided via cloud computing services, that

are enabling technologies for the computational IoE infrastructure,

including computational resource-on-demand services, micro-services

and so-called server-less and edge computing (Azodolmolky, Wieder, &

Yahyapour, 2013).

Second, Big Data is a key enabling technological development

driving the growth of smart things. The technological advancement has

proceeded in two directions: enhancing the quality of sensing resulting

in higher data quantities, and improving algorithms to interpret the

massive amounts of sensing data. The amount of continuously gathered

data has allowed researchers to use statistical methods that were pre-

viously considered to be of lesser importance as, due to the lack of data,

they often resulted in overﬁtted models, as was the case with multi-

layered neural networks (Hippert, Pedreira, & Souza, 2001). This suc-

cess of Big Data is possible due to advancements in distributed systems,

as we now know how to build scalable fault-tolerant applications,

making storing (e.g., databases) and processing of large amounts of data

possible. When it is not feasible to transmit all this data over networks

to be analyzed in centralized data centers, the physical things that

generate data can carry out computations at the source, which is made

possible by advances in Edge Computing (Shi, Cao, Zhang, Li, & Xu,

2016). Additionally, multilayered neural networks, deep learning, and

reinforcement learning use the copious amounts of available data to

train smart systems. This allows smart things to exhibit adaptive abilities

as they learn from the massive amounts of historical data. For example,

Big Data analysis is used to reﬁne an objective function based on real-

world optimization, such as improving user comfort, energy eﬃciency,

or other human deﬁned aims.

Third, the foundations of smartness has been long been studied in

the context of theoretical Artiﬁcial Intelligence (AI), where smart things

are understood as objects that are sensing, reasoning, and performing

actions based on the input data to reach a certain predeﬁned goal.

Smartness in this particular case implies autonomous behavior, and

often involves various AI algorithms. The concept of a rational, au-

tonomous agent is not new, and has been studied since the early days of

AI, resulting in a number of specialized research areas focusing on

diﬀerent aspects. For example, research into AI planning (Ghallab, Nau,

& Traverso, 2004) examines how to achieve a given goal by con-

structing a sequence of actions from the initial state, and forms a re-

presentation of reasoning (Cohen & Feigenbaum, 2014; Kaldeli,

Lazovik, & Aiello, 2016). Research on machine learning employs ad-

vanced statistical methods both to improve quality of the interpretation

of the sensing data and to enable smart agents to learn how various

actions contribute to the achievement of the desired objectives, as ex-

empliﬁed by, for example, reinforcement learning (Leonetti, Iocchi, &

Stone, 2016). Research on knowledge representation uses diﬀerent

logic models to describe the world so that agents can interpret facts

about the world and make reasoned decisions (Bench-Capon, 1990).

Such ﬁelds study both the behavior of individual agents but also multi-

agent systems, where diﬀerent agents interact to achieve a common

goal (Pinyol & Sabater-Mir, 2013).

Finally, the importance of semantic interoperability –the ability of

heterogeneous devices to understand each other –was clearly under-

stood from the early beginnings of the Web, and was further developed

after the success of online services, resulting in a Semantic Web with a

number of standards, largely based on the theoretical work in logic and

knowledge representation to enable the reuse of reasoning algorithms

(Poggi et al., 2008). However, the lack of widely adopted IoT standards

does not allow for global interoperability at the current time, at least

not at the same level of seamless interconnectivity that the traditional

Internet has. At this point, vendor-speciﬁc IoT platforms are used to

bypass this issue. The global interoperability over the Web was ﬁrst

widely possible after the introduction of service-oriented computing

relevant web service protocol stack. More recently, distributed ledger

technologies, such as the blockchain, allow for decentralized coopera-

tion between things whereby the interoperability is arranged within

subsystems that interact via smart contracts (Anjum, Sporny, & Sill,

2017). Successful interoperability results in so-called smart

D.J. Langley, et al. Journal of Business Research xxx (xxxx) xxx–xxx

environments, where multiple collaborative devices are seamlessly in-

tegrated and work together towards a common goal (Kaldeli et al.,

2016). While the semantic web has been successful, we still cannot say

that the problem of a general semantic interoperability was solved, as

diﬀerent systems still require a lot of expert knowledge to realize some

form of semantic interoperability, such as the well-known IBM jeo-

pardy-solving computer (Tesauro, Gondek, Lenchner, Fan, & Prager,

2013).

Taken together, the technological developments described here

enable physical objects to be endowed with wholly new levels of

smartness.

4. A taxonomy of smart things in the IoE

4.1. Taxonomy development

New IoE products and services are starting to appear regularly in a

wide range of markets. With this rapidly expanding range of IoE ap-

plications, it becomes diﬃcult to identify where new applications be-

long in the expanding IoE-enabled business ecosystem. Researchers,

and IoE developers and business strategists need to be able to determine

where a new IoE innovation ﬁts alongside existing applications in order

to ascertain if it enables something entirely new and unique, a sig-

niﬁcant variation of an existing application, or just a rehash of what we

already have. A concise, comprehensive, and extendible taxonomy of

IoE applications would provide a basis for making this determination.

In general, a taxonomy is understood as classiﬁcation of a set of

objects in a domain of interest (Nickerson, 2013). According to Baily

(1994), taxonomies can be distinguished from typologies in that the

former is derived empirically and inductively and the latter con-

ceptually and deductively. However, Nickerson (2013) pleads for

common recognition over precision and uses the term taxonomy for

inductively, deductively and intuitively derived classiﬁcations. In the

management literature the importance of taxonomies is recognized and

various taxonomies exist, including software development methods

(Blum, 1994), organizational structures (Mintzberg, 1985) and mobile

applications (Nickerson, Varshney, Munterman, & Isaac, 2007). Taxo-

nomies help researchers to understand and analyze complex and

emerging domains (Nickerson et al., 2007), they provide a structure

and an organization to knowledge of a ﬁeld, and they enable re-

searchers to study the relationship among concepts (Glass & Vessey,

1995).

Therefore, in this paper we propose a taxonomy of various levels of

smartness of things in the IoE, with which we can describe the business

model implications. Such a taxonomy makes it easier to compare dif-

ferent IoE applications and benchmark the level of maturity of an IoE

ecosystem built around interconnected smart things. It may also pro-

vide guidance to strategists and IoE innovation developers to realize the

potential beneﬁts of IoE.

Rijsdijk and Hultink (2009) deﬁne product smartness as a sum of

capabilities over several dimensions, such as autonomy, adaptability,

reactivity, multi-functionality, ability to cooperate to achieve a common

goal, human-like interaction, and personality.Human-like interaction and

personality mainly refer to the perception of the end user, and as such, in

this paper we focus on the ﬁrst ﬁve capabilities. We also introduce an

additional orthogonal dimension based on connectivity that represents

the potential for smartness of the system where the smart things reside

(together with people, organizations, businesses, etc.). As we concluded

in the consideration of the theoretical lens of service value ecosystems,

the extent to which smart things are connected determines the reach

and richness of the service ecosystem, including ﬂows of resources and

information, and it thus determines the nature of the networked busi-

ness model. Consequently, we consider connectivity as a main char-

acteristic of smartness at a system level. It directly inﬂuences the

complexity of possible interactions within the environment, thus de-

ﬁning also the complexity and the type of business landscape.

For the ﬁrst dimension of capabilities, we deﬁne reactive smart

things as having the ability to immediately adjust to a changing en-

vironment. Adaptive smart things have the longer-term ability to adjust

their behavior to changes, such as by learning from historical data or

usage patterns. Autonomous smart things have the ability to act in-

dependently, without direct intervention from human agents.

Cooperative smart things have the ability to interact with other con-

stituents of the IoE in order to jointly work towards a uniﬁed objective.

The ﬁnal smartness capability from Rijsdijk and Hultink (2009) is multi-

functionality which refers to the ability to support and combine several

functions in a single device. However, due to the other dimension for

our taxonomy, connectivity, we no longer see the relevance of focusing

on multi-functional things when a set of connected, uni-functional,

things can amount to the same. As such, we drop multi-functionality

from our taxonomy. Therefore, we utilize four smartness capabilities

that denote how smart things can be reactive, adaptive, autonomous

and collaborative. It may be clear that each of these capabilities is a

technological achievement in its own right, a fact highlighted in our

earlier discussion of enabling technologies, but it is in their combina-

tion that the highest levels of smartness may be achieved.

For the second dimension, connectivity, we deﬁne three levels that

are relevant for the IoE. A closed system refers to connectivity between a

limited and predeﬁned set of things whereby physical or technical

measures are taken to prevent inﬂuence from external factors. This is

then a self-contained network and it may be considered not to be a part

of the IoE at all. An open system with a restricted communication protocol

refers to connectivity between a larger set of things that are not pre-

deﬁned but that may only connect together if they adhere to a speciﬁc

set of rules and standards. Such rules and standards may be designed to

intentionally restrict connectivity access to a subset of IoE-connected

things, but it may also be a temporary state before globally agreed upon

protocols and standards are developed. Finally, an open system with full

interoperability refers to the situation when connected things have un-

restricted access to each other and to all constituents of the IoE,

whereby each thing clearly understands the communication of all other

things.

By combining these two dimensions of capabilities and connectivity,

we may deﬁne increasing levels of smartness for things in the IoE, as

they become more able and more connected. In order to develop our

taxonomy and to explore the implications of smartness for business

models we now describe a hypothetical example, in the form of a

vignette on business models related to waste collection and recycling, to

illustrate the consequences of smart things for businesses in practice.

4.2. Vignette on waste collection and recycling

0. Traditional non-smart solution. There is a waste collection bin to

collect waste plastic at a municipal site. The physical things in the waste

collection and recycling process are not smart. An organization that

wants to buy recycled plastic makes contact with the municipality and

agrees on the amount, quality, price and other terms. The business

models are traditional, trade-based models focused on individual

transactions.

1. Things are reactive, but not connected to the wider IoE. The waste

collection bin is equipped with sensors and a closed-system commu-

nication system. This makes the bin somewhat smart as it is able to

measure characteristics of the waste as it is deposited, such as the

weight and quality. On a daily basis, an operator drives to the muni-

cipal waste collection site and records the data about the deposited

plastic from the built-in sensors, using a hand-held recording device

made speciﬁcally for this purpose. Back at the oﬃce, this device con-

nects to a database and when an organization wants to buy recycled

plastic, they can see a representation of the data via a website, and note

that this was automatically generated. Due to the data accuracy and

frequency, the municipality can develop a new business model, such as

allowing ﬁrms to bid for plastics of diﬀerent qualities as they become

D.J. Langley, et al. Journal of Business Research xxx (xxxx) xxx–xxx

available. The eﬀects of this change are mostly felt at the level of in-

dividual organizations and the overall waste collection and recycling

system is unaﬀected.

2. Things are reactive and adaptive, connected to the IoE via a protocol

with pre-deﬁned rules. The waste collection bin continually measures the

weight, types and quality of plastic that is deposited in real time, and

via a network connection, sends that data to the database. Using image

recognition software, the bins are able to learn to separate diﬀerent

types of used plastic. The operator has now lost this job of reading out

the weight, and other technicians have acquired new jobs to develop

and build the more advanced waste bins that include sensors, cameras,

network connections, etc. For organizations that want to buy recycled

plastic, the municipality can now oﬀer access to the database by pro-

viding a remote access protocol. Their business model can change to

take advantage of the adaptability of the waste bins and they can cus-

tomize their oﬀering to diﬀerent customers, to meet each party’s spe-

ciﬁc requirements. The eﬀects of these changes are felt by individual

ﬁrms but also through the plastic recycling industry, as diﬀerent parties

are more able to specialize and ﬁne-tune their service oﬀerings.

3. Things are autonomous and more broadly connected to other con-

stituents of the IoE. The waste collection bin is in contact, via a trading

platform, with transport trucks of known organizations in a business

network that want to buy recycled plastic. When a buying organization

wants plastic, their trucks connect to the waste collection bin (via a

standardized application programming interface) and a transaction

takes place autonomously, according to predeﬁned contractual

arrangements. The ﬁrms involved and the municipality have delegated

the day-to-day negotiation and transaction completion to the smart

waste bins and trucks via logistics systems, and trust is ensured by only

providing access to this open system to predeﬁned parties and their

smart things. The eﬀects of these changes may be felt at the micro level

of the individual organizations, as they streamline their business

models, and at the meso level of the plastic recycling industry, as the

autonomous things in the IoE make eﬃcient choices and learn from the

increasing amounts of historical data. Additionally, there may be eﬀects

felt more widely at the macro level across industries, as manufacturers

change their designs for all manner of objects to make use of the im-

proved accessibility to cheap, high quality recycled plastic.

4. Things are autonomous and collaborative, working together to achieve

mutual objectives with full interoperability through the IoE. There is a

transactional system within which the waste collection bin is one agent.

There is a globally standardized way of exchanging data, based on se-

mantic interoperability, allowing for automated transactions for data or

recycled plastic. The use of the data or plastic may not have originally

been foreseen and could be used in a totally diﬀerent context; for ex-

ample, the municipality may design a smart solution, using data on the

amount and type of plastic being recycled to understand the local po-

pulation in order to designate where to locate a new children’s play-

ground. Smart contracts between agents may be negotiated in real time

within restrictive boundaries set by organizations or regulators, ac-

cording to utility functions, in order to achieve speciﬁc objectives. The

smart things and the contracts they work with are designed to achieve

Fig. 1. Taxonomy of the increasing levels of smartness for things in the Internet of Everything, (A) their implications for business models (BMs), and (B) the

abstraction level of their eﬀects in the economy.

D.J. Langley, et al. Journal of Business Research xxx (xxxx) xxx–xxx

given goals of increasing plastic recycling, plastic re-use, etc., and they

collaborate to achieve these aims. The business models of organizations

that make use of the beneﬁts oﬀered by these collaborative, openly

connected smart things are ﬂuid and ﬂexible, regularly changing to

adapt to new circumstances. In this way, the strategic ﬂexibility of

organizations becomes highly important. The eﬀects of these changes

are widely felt throughout the economy as this recycling system and

many other similarly smart, interconnected systems interact. Industry

boundaries become less relevant as data and materials ﬂow freely

across domains. Institutional arrangements, including regulations, also

adapt to keep pace with the emerging needs of the society.

4.3. A taxonomy of smart things and their consequences for business models

The vignette above helps to explain the implications of increasingly

smart things for business models. We now present our taxonomy in

Fig. 1, which shows several levels of smartness for things on the two

dimensions of capabilities and connectivity that are missing in tradi-

tional, non-smart things. On top of these two dimensions, we add two

types of consequences of smartness, following the insights from the

vignette. First, the implications for business models are plotted. Second,

the abstraction level - micro, meso or macro - where the consequences

are felt are plotted.

The taxonomy illustrates the impact of the IoE on business models

from a service ecosystem perspective. When objects are neither smart

nor connected, business models of individual organizations are not

impacted, given that the ﬂow of operant resources and information

remains unaﬀected. However, when objects become more capable and

more connected, the ﬂow of operant resources and information will

change, therewith aﬀecting interactions between the business models at

the micro level. This can create complementarities or conﬂicts at the

meso level where smart things become actors in service ecosystems that

span diﬀerent domains or industries.

So, following Fig. 1A, when objects connected in the IoE have a low

level of smartness then the most valuable added resource is accurate

data, conveying valid, reliable and up-to-date information on which

new value propositions can be built. Where objects are adaptive and

connected through open systems, then organizations become able to

oﬀer services utilizing customizable functions and facilities, guiding

smart things to adapt to changing needs or contexts. Where objects can

make autonomous decisions, connected in fully interoperable open

systems, then the types of business models made possible becomes

further enhanced; customers may negotiate with the smart things to

make use of the ﬂow of information and co-create value even more

eﬀectively. Finally, at the highest level of smart things, with highly

autonomous, highly collaborative things connected to almost endless

other data streams, ﬂuid and ﬂexible business models become possible.

This means that the notion of a ﬁxed service oﬀering becomes obsolete

and organizations will need to embrace the notion of constantly chan-

ging, highly personalized value propositions.

Following Fig. 1B, the structural changes experienced within and

across industries are also dependent on the levels of smartness that

things acquire. At the lower levels of smartness, individual organiza-

tions can continue to operate as they have before, making use of the IoE

to enhance eﬃciency and the quality of their decision-making

(Drnevich & Croson, 2013). However, once the smart things become

adaptive and autonomous, highly connected to data systems across

multiple domains, then we will observe changes to the underlying

working and conﬁguration of industries and institutional arrangements,

much as internet banking catalyzed a shift in retail banking. Finally, at

the highest levels of the smartness of things, macro-level changes will

result in industry convergence, new economic patterns and the emer-

gence of wholly new institutions.

When objects take an active part in creating value in the context of

their own experience and usage, their connectivity enables resource

ﬂows, resulting in both complementarity and conﬂict in practice. Not

all ﬁrms aim for full interoperability - Apple being a case in point - and

path dependencies from existing business models based on unconnected

objects can result in other conﬂicts that may impede further smartness

from emerging. For example, it is well known that Apple devices can

only print through AirPrint, Apple’s specialized technology. Thus while

it is technically possible to print a document on a printer that does not

support such a technology, it is unclear which party, Apple or, for ex-

ample, Hewlett Packard, would create the connectivity and, by so

doing, carry out the necessary “institutional work”(Lawrence &

Suddaby, 2006). Institutional work refers to “the broad category of

purposive action aimed at creating, maintaining, and disrupting in-

stitutions”. Such conﬂicts may be resolved through changes to business

models at the micro level. At the meso level, institutional work may be

conducted, i.e. actors actively changing the rules and practices. At the

macro level, new institutional arrangements may then emerge, with a

completely new system of rules to govern social interactions. Thus, we

propose that a systemic perspective of smartness embedded in the IoE,

as we present here, captures the dynamic nature of value creation and a

more comprehensive view of the implications at diﬀerent levels (Vargo

& Lusch, 2016).

5. Discussion

The aim of this paper is to describe how smart things in the IoE may

impact business models and to propose avenues for future research. In

this regard, we did not only focus on the impact of the IoE on the

business models of a single ﬁrm. Instead, we adopted a service eco-

system perspective that allowed us to examine how smart things aﬀect

resource ﬂows in a networked system on micro, meso and macro levels.

Using a vignette, we argued that the impact of the IoE on business

models increases with higher levels of smartness. We developed a tax-

onomy classifying smart things based on their levels of capability and

connectivity. We further proposed that smart things with an increas-

ingly higher level of smartness will have an impact on an increasingly

higher abstraction level within economies, whereby the highest levels

of smartness will have implications at the macro level across industries,

resulting in the emergence of new institutional arrangements. Less so-

phisticated smart things will also aﬀect business models but most likely,

only at the micro level of individual organizations. Drawing on our

analysis, we now derive propositions about how smart things in the IoE

may impact business models and the process of value creation on the

micro, meso and macro levels. Taken together, the propositions can be

seen as a research agenda and used as starting point for scholars to

develop new knowledge about business models in the IoE that is re-

levant from both scientiﬁc and management perspectives.

5.1. Micro-level propositions

From a micro perspective, endowing things with smartness may

radically alter not only the service provision to the customer, but also

the work environment of the frontline service employee.

From a customer perspective, endowing things with smartness en-

ables better, more customized and personalized products and services

that provide improved convenience (Rust & Huang, 2014). They also

enable the customer to delegate tasks, thus saving time and eﬀort. For

instance, when current connected cars, without the intervention of the

customer, arrange for a needed software update, they save the customer

the bother of interacting with the car maintenance ﬁrm (Porter &

Heppelman, 2014). These advantages of smartness drive customer ac-

ceptance of smart objects, which is likely to improve with increasing

levels of smartness. However, at very high levels of smartness, issues

such as security, privacy and trust become more relevant

(Papadopoulou, Kolomvatsos, Panagidi, & Hadjiefthymiades, 2017).

According to our taxonomy, for example, things with a low level of

smartness are only reactive while those with a high level of smartness

are autonomous and able to collaborate in order to achieve their goals.

D.J. Langley, et al. Journal of Business Research xxx (xxxx) xxx–xxx

To provide the necessary algorithms with enough input, they need to

exchange data about their environment and stakeholder interactions

with other smart things in their network. It becomes apparent that in

particular, the exchange of data between diﬀerent smart things poses

security risks such as eavesdropping, hacking, unauthorized access to

data, and unauthorized access to devices (Porras, Pänkäläinen, Knutas,

& Khakurel, 2018). At very high levels of smartness where customer

data is potentially shared with the many connected objects and parties,

customers may experience fear of data loss and privacy concerns. To

develop successful IoE business models, it is therefore important to

consider the trust that consumers have towards smart things but also

towards the diﬀerent parties who use the enormous amount of data

collected by smart things (Abomhara & Koien, 2014; Andrea,

Chrysostomou, & Hadjichristoﬁ, 2016). Prior research recommends that

businesses should communicate in a transparent way, what kind of data

are collected and for which purposes this data is used by diﬀerent

parties (Roman, Najera, & Lopez, 2011). Furthermore, it has been

suggested that users should be able to decide for themselves which data

they want to share. However, for businesses this represents a challenge

in developing ﬂexible business models that still work if consumers

allow smart things to collect only a selection of the requested data.

Concerns about the security of data transmission and the usage of that

data by ﬁrms may decrease customer acceptance of smart things (Rust,

Kannan, & Peng, 2002), making an inverted U-shape relation between

smartness level and customer acceptance likely.

Proposition micro level 1: There is an inverted U-shape relation be-

tween the level of smartness and customer acceptance, with low acceptance

for very low and very high levels of smartness.

From a frontline employee perspective, smart things that comple-

ment the operations of human employees can be highly valuable tools

because they enable the employees to do their jobs better or with less

eﬀort. Smart technology can free up time for other tasks, particularly

those in which humans excel, such as building relationships with cus-

tomers. This expectation is in line with previous literature showing that

adoption of IT technology leads to improved customer service and

adaptability to customer requirements (e.g. Ahearne, Jones, & Mathieu,

2008).

However, to what extent this advantage can be realized is critically

dependent on how smart the technology is. With lower smartness le-

vels, employees have to put a lot of time into preparing all the data and

systems for the smart thing, hence becoming its assistant. Medical

professionals for instance may have to spend signiﬁcant time to collect

patient data and make them accessible to the system. Once the things

achieve a higher level of smartness, they can prepare their own input

and leave the people to interpret, create, and handle their tasks fruit-

fully (Huang & Rust, 2018; Lingo & Bruns, 2018). With very high levels

of smartness, frontline service employees may feel that they lose agency

and decision-making power to the smart object taking over the func-

tions and decisions they used to make, triggering fear of job loss (Huang

& Rust, 2018). Once again, we propose an inverted U-shape relation

between the level of smartness and acceptance by frontline service

employees.

Proposition micro level 2: There is an inverted U-shape relation be-

tween the level of smartness and acceptance by frontline service employees,

with low acceptance for very low and very high levels of smartness.

5.2. Meso-level propositions

It is also important to note that IoE business models are not static.

Instead they are continually being enacted through the resource in-

tegration practices of the actors. Indeed business models “deﬁne the

resources that an individual market actor possesses and the ways that actor

can interact with other actors—and their resources”(Storbacka &

Nenonen, 2011, p. 247). In the IoE, resource ﬂows from connected and

smart things often include data ﬂows that dictate how connectivity

creates value and how the business models that integrate data create

complementarity or conﬂicts at the meso level.

While business models may create conﬂicts at the meso level,

changes of the institutions, and therewith the system of rules governing

interactions, are often instigated by humans. Human capabilities have

the capacity to mitigate risks and moderate the eﬀect of smartness to

ensure value creation occurs, and they are also able to do the institu-

tional work required to develop new arrangements (Lawrence &

Suddaby, 2006). For example, connecting cameras, door bells and

mobile phones at the micro level can result in a home security system

but this may not be acceptable to insurance companies, whose business

models rely on risk mitigation according to known industry standards at

the meso level. However, through human use and re-use of connected

objects, meso-level norms emerge that may then result in business

models adapting at the micro level. Thus, an understanding of the IoE

must include the role of humans, as they play a key role in the way the

service ecosystems evolve (Ng & Wakenshaw, 2018; Vargo & Lusch,

2016). Humans therefore play a dual role; that of a moderator of con-

nected business models at the meso level as well as an instigator of

conﬂicts to business models at the micro level, driven by their need to

appropriate connected resources to aid value creation. This duality of

being perpetrator of conﬂicts as well as the moderator of risks is an

important component of the IoE.

Proposition meso level:Institutional work at the meso level moderates

the positive relationship between the level of smartness and ﬁrms’ability to

successfully develop and apply new business models.

5.3. Macro-level propositions

As a third perspective in this discussion, we consider key research

challenges at the macro level, as the IoE and smart things alter value

ecosystems within industry sectors as well as between industry sectors.

The taxonomy proposed in this paper demonstrates that as connected

things acquire higher levels of smartness (see Fig. 1), multiple con-

stituents within and between industry sectors become part of a single

IoE network, making use of shared data and developing ﬂuid and

ﬂexible business models. This leads to industry convergence (Sick,

Preschitschek, Leker, & Bröring, 2019), new networked business models

(Adner, 2017; Bankvall et al., 2017; Oskam et al., 2018), and new

service ecosystems (Hacklin, Battistini, & von Krogh, 2013). Established

industry boundaries become blurred and fragmented and industries no

longer exist in separate spheres. New industry outsiders enter markets

and deﬁne new paradigms, often in the form of IoE-enabled services

instead of - or alongside - physical products. This implies not only that

individual organizations must embrace the emergence of smart things

and adapt their business models, their operations and their value eco-

systems, but also that whole sectors or economies must do the same

concurrently. So a key challenge appears to be guiding organizational

decision-making about which networks to participate in, and how to

ﬁnd the right partners for value co-creation. Such a move into unknown

territory brings signiﬁcant diﬃculties in aligning institutional ar-

rangements between hitherto unconnected sectors (Besharov & Smith,

2014).

Despite these imminent transitions, many ﬁrms struggle to re-

cognize the dynamics of IoE-enabled industry convergence and its re-

lated challenges and opportunities (Kim, Hyeokseong, Kim, Lee, & Suh,

2015). The disruption resulting from industry convergence creates a

complex, ﬂuid and uncertain environment, where enduring strategic

choices are hard to make (Hacklin et al., 2013). For example, car

manufacturers produce smart cars, which creates intra-industry con-

nectivity between automotive actors: petrol stations, maintenance

companies, parking garages, car sharing ﬁrms and traﬃc information

providers. However, the IoE will also enable inter-industry con-

nectivity, connecting cars with city planners, restaurants, healthcare

providers, home delivery services, and so on. This example demon-

strates that car manufacturers have to develop ﬂuid and ﬂexible busi-

ness models that determine how they can co-create value within an

D.J. Langley, et al. Journal of Business Research xxx (xxxx) xxx–xxx

emerging open, interoperable global network. Such strategies have to

be focused on developing services that add value through sharing skills,

resources and knowledge with other actors (Barrett et al., 2015). Es-

tablished and ‘narrow’intra-industry institutional arrangements are

threatened by this development.

Proposition macro level 1: The IoE, and its related smart things, will

result in industry convergence and blurring industry boundaries.

The taxonomy shown in Fig. 1 indicates that the highest levels of

smartness of things in the IoE are brought about by connected things

exhibiting an ability to adapt to unknown or unexpected circumstances,

to act autonomously and to collaborate with other constituents. In turn,

this implies that networks of organizations too will need to develop and

apply related strategic competences, to have ﬂexible organizational

structures, relationships and processes, and to adapt quickly as new

data-driven insights emerge. Apart from new entrants, most ﬁrms are

limited by path dependencies (Wirtz et al., 2010), making large-scale or

rapid changes highly challenging. We can expect that the ﬁrms that are

most able to follow a path of stakeholder management towards a net-

worked business model will be best placed through mutual beneﬁt-

sharing agreements.

Proposition Macro 2: Networked business models, as opposed to single-

ﬁrm business models, will become more prevalent and more successful in the

IoE.

Finally, at the macro level, literature has debated ways in which

digitalisation, including the IoE and highly smart things, will lead to

what is known as a ‘winner takes all’(WTA) situation (Cennamo &

Santalo, 2013; Frank & Cook, 1995; Loebbecke & Picot, 2015), which

may result in wholly new institutional arrangements emerging

(Hodgson, 2015; North, 1991). The IoE reduces both marginal pro-

duction costs in markets, thereby increasing economies of scale, and

distribution costs for digital products and services. Both of these then

result in a centralization of power with a small number of market

players dominating (Loebbecke & Picot, 2015). Research has shown

such centralization processes to be counter-beneﬁcial to macro-level

economic well-being (Rosen, 1981), leading to worsening employment

conditions, increasing power asymmetries and more social inequality.

However, Cennamo and Santalo (2013) suggest that platform compe-

tition is shaped by important strategic trade‐oﬀs and that the WTA end-

game will not be universally successful. Indeed, in the speciﬁc area of

IoT we may see a high level of industry specialization whereby, if the

costs of multi-homing are low, a more fragmented competitive land-

scape could emerge. This is reﬂected in the early period of IoT platform

development which lacks any clear trend towards consolidation. Our

taxonomy (Fig. 1) indicates that there is great potential to be had from

smart things in the IoE, but this necessarily implies that the potential

will only be realized when macro-level institutions enable all relevant

parties to share data. Highly powerful monopolists can coerce other

players to some extent, but we may expect new institutional arrange-

ments to develop technical, legal and managerial solutions for each

party to maintain sovereignty over its own data, to control which other

parties can access it, for what purposes and at what price.

Proposition Macro 3: At higher levels of smartness, industry sectors

will succumb to the ‘Winner Takes All’pattern of power centralization unless

new institution arrangements emerge to enable data sovereignty solutions.

6. Outlook

This article explores how the IoE will impact on the business world,

with a speciﬁc focus on the business model. Strategic theory following

the service-dominant logic (Vargo & Lusch, 2017), posits that the pro-

ducts and things people buy are simply carriers of competences and

functionality that form a service that users apply in the context of their

use situation. We have discussed how this situation will change as the

IoE leads to increasing levels of smartness in the things that we buy and

use. Even though the dynamics of these changes are mainly driven by

technical progress such as interconnectivity, big data, artiﬁcial

intelligence, and semantic interoperability, organizations and institu-

tions also have important roles to play in inﬂuencing the way the IoE

will change societies.

To date, there is a lack of theory and methodologies to help orga-

nizations make the right strategic choices as we enter the era of the IoE.

Using theory-based insights from two related disciplines, and from in-

ductive insights from a vignette, we propose a taxonomy of con-

ceptually diﬀerent levels of smartness, and we explore the implications

of the IoE for business models and value creation on the micro, meso

and macro levels.

Based on the suggested taxonomy and the theorizing of proposi-

tions, researchers can be guided to conduct case studies in order to

more deeply examine the nature of smartness and validate its diﬀerent

levels as developed in our taxonomy. Case studies could also shed light

on the consequences of things getting smart for companies, consumers

and other stakeholders. Scales and measures to, for instance, assess the

level of smartness could be developed in quantitative studies. The IoE is

expected to bring about unprecedented societal change, opening up

new opportunities and posing new challenges. Research from diﬀerent

disciplines such as economics, computer science, psychology, law and

ethics is needed to better understand and inﬂuence future develop-

ments in the IoE.

Acknowledgements

This paper would not have been possible without the help of Thijs

Broekhuizen, Manda Broekhuis and Maarten Gijsenberg, who hosted

the Digital Business Models Thought Leadership Conference (Groningen

University, Netherlands, April 2018) that brought the authors together,

and stimulated a discussion with practitioners.

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This article investigates challenges pertaining to business model design in the emerging context of the Internet of Things (IOT). The evolution of business perspectives to the IOT is driven by two underlying trends: i) the change of focus from viewing the IOT primarily as a technology platform to viewing it as a business ecosystem; and ii) the shift from focusing on the business model of a firm to designing ecosystem business models. An ecosystem business model is a business model composed of value pillars anchored in ecosystems and focuses on both the firm's method of creating and capturing value as well as any part of the ecosystem's method of creating and capturing value. The article highlights three major challenges of designing ecosystem business models for the IOT, including the diversity of objects, the immaturity of innovation, and the unstructured ecosystems. Diversity refers to the difficulty of designing business models for the IOT due to a multitude of different types of connected objects combined with only modest standardization of interfaces. Immaturity suggests that quintessential IOT technologies and innovations are not yet products and services but a "mess that runs deep". The unstructured ecosystems mean that it is too early to tell who the participants will be and which roles they will have in the evolving ecosystems. The study argues that managers can overcome these challenges by using a business model design tool that takes into account the ecosystemic nature of the IOT. The study concludes by proposing the grounds for a new design tool for ecosystem business models and suggesting that "value design" might be a more appropriate term when talking about business models in ecosystems.

Investigating the business potential of Internet of Things

Conference Paper

Full-text available

Sep 2017

Security In The Internet Of Things - A Systematic Mapping Study

Conference Paper

Full-text available

Jan 2018

Artificial Intelligence in Service

Article

Full-text available

Feb 2018
J SERV RES-US

Artificial intelligence (AI) is increasingly reshaping service by performing various tasks, constituting a major source of innovation, yet threatening human jobs. We develop a theory of AI job replacement to address this double-edged impact. The theory specifies four intelligences required for service tasks—mechanical, analytical, intuitive, and empathetic—and lays out the way firms should decide between humans and machines for accomplishing those tasks. AI is developing in a predictable order, with mechanical mostly preceding analytical, analytical mostly preceding intuitive, and intuitive mostly preceding empathetic intelligence. The theory asserts that AI job replacement occurs fundamentally at the task level, rather than the job level, and for “lower” (easier for AI) intelligence tasks first. AI first replaces some of a service job’s tasks, a transition stage seen as augmentation, and then progresses to replace human labor entirely when it has the ability to take over all of a job’s tasks. The progression of AI task replacement from lower to higher intelligences results in predictable shifts over time in the relative importance of the intelligences for service employees. An important implication from our theory is that analytical skills will become less important, as AI takes over more analytical tasks, giving the “softer” intuitive and empathetic skills even more importance for service employees. Eventually, AI will be capable of performing even the intuitive and empathetic tasks, which enables innovative ways of human–machine integration for providing service but also results in a fundamental threat for human employment.

The interaction between network ties and business modeling: Case studies of sustainability-oriented innovations

Article

Full-text available

Mar 2018
J CLEAN PROD

A stream of literature is emerging where network development and business modeling intersect. Various authors emphasize that networks influence business models. This paper extends this stream of literature by studying two cases in which we analyze how business modeling and networking interact over time. We propose the concept ‘value shaping’ to idescribe this interaction. Value shaping refers to the mutually constitutive process in which on the one hand networking helps to refine and improve the overall business model and on the other hand an improved business model spurs expansion of the network. We identify five micro-level processes through which value shaping occurs. Value shaping is particularly relevant for sustainability-oriented innovations, to help clarify all the types of financial, social and environmental value to which a business model may contribute.

Blockchain Standards for Compliance and Trust

Article

Full-text available

Jul 2017

Blockchain methods are emerging as practical tools for validation, record-keeping, and access control in addition to their early applications in cryptocurrency. This column explores the options for use of blockchains to enhance security, trust, and compliance in a variety of industry settings and explores the current state of blockchain standards.

A new framework to assess industry convergence in high technology environments

Article

Aug 2018
TECHNOVATION

The process of convergence, from science and technology convergence to that of markets as well as entire industries can be witnessed in a range of different high technology environments such as IT and NanoBiotech. Although this phenomenon has been subject of analysis in an increasing number of studies, the notion of industry convergence – the final step of a full convergence process - still lacks a common definition. The missing conceptual definition of what industry convergence really is and how it can be assessed impedes both analyses and monitoring - let alone its anticipation. To address the missing conceptual definition of the final step in convergence, this paper seeks to develop a framework based on novel indicators that enable identifying and monitoring trends of industry convergence in high technology environments. Building on indicators in the domain of collaboration, a framework, which distinguishes different stages and types of industry convergence is developed. Subsequently, the newly developed framework is empirically illustrated in the area of stationary energy storage based on publicly available data. To this end, the full text database Nexis is used to conduct a search in news reports on collaborations in the domain of stationary energy storage. The study contributes to the growing body of convergence literature by providing a novel framework allowing the identification of not only industry convergence as the final step of the convergence process but also the classification of its type. Practical implications include an orientation for companies in converging environments on when and how to close the resulting technology and market competence gaps.

Service Ecosystems: A timely worldview for a connected, digital and data-driven economy

Chapter

May 2018

We live in a world of hyper-connectivity with systems that connect people and things embedded in people’s lives, in themselves nested within societal living and the ecology of our planet. As a service researcher, we need to interrogate the paradigmatic assumptions and consider the world views for conducting good service research. After such endeavours, we propose that service ecosystem is a timely worldview needed for service research for a connected, digital and data-driven econo- my. Service ecosystem allows the clarification of interactions not merely between actors, but within a system and between systems. This “ecosystem” view can provide a framework for studying wider systems, or the interaction and value co-creation among multiple service systems (Vargo and Lusch, 2011b). The paper also demonstrates the need to apply service ecosystem worldview in such research areas as: (1) an increasingly-connected world; (2) designing future “Things”; and (3) the data economy.

Dynamic Patterns of Industry Convergence: Evidence from a Large Amount of Unstructured Data

Article

Jan 2014

Because of the accelerated life cycle in technology and correspondingly rapid technological saturation in markets, firms are not only accelerating the rate of technological innovation but also expanding the scope of their products or services by combining product or service features of other markets, which eventually leads to industry convergence. However, despite the significant impact of industry convergence on the economy, our understanding of the phenomenon is still limited because previous studies explored only a few cases and come largely from the technological perspective. Therefore, it is still questionable whether industry convergence is a general phenomenon that is prevalent across entire industries. In this paper, we analyze the phenomenon in entire U.S. industries, focusing on its trends and patterns. To do so, we conduct a co-occurrence-based analysis of text mining for a large volume of unstructured data – 2 million newspaper articles from 1989 to 2012 – and suggest using an industry convergence (IC) index based on normalized pointwise mutual information (PMI). We find that overall industry convergence is increasing over time. Moreover, the rate of the increase has been greater within industry than between industries at a given industry level. However, when we cluster the dynamic patterns of industry convergence among industry pairs, the patterns are mixed, and, while some industry groups are converging over time, others are stationary. These findings suggest that significant transformation is under way in the economy, but this phenomenon is not yet prevalent across entire industries. In addition, this study provides a method for anticipating the future direction of industry convergence.