The Socio-technical Impact of the Internet of Things: An Exploratory

Mixed Methods Research

Albert Boonstra

and Dustin A. Wiktor-Steffens

1,2 b

Faculty of Economics and Business, University of Groningen, Nettelbosje 2, Groningen, The Netherlands

School of Economics, Fudan University, 600 Guoquan Road, Shanghai, China

Keywords: Internet of Things, Socio-technical Systems, Intentionality, Materiality.

Abstract: This study explores how the Internet of Things (IoT) impacts the socio-technical system of organizations. The

paper adopts a mixed methods research with a qualitatively driven approach. Data from 21 interviews with

experts in the field of IoT and an online survey with 123 IoT professionals were analyzed. Leonardi’s Socio-

Technical System Model (2012) was applied as a lens to examine how IoT influences the organizations’ social

subsystem and how that, in turn, affects both the materiality of IoT and users’ intentionality in the technical

subsystem. The results suggest transformed roles, potentially flattened hierarchies, decreased privacy, and

increased transparency to be the main effects. While apparent changes in the social subsystem cause perceived

threats that strongly influence users’ intentionality, they do not certainly affect IoT’s materiality. Noteworthy,

however, is that irreplaceable users reportedly have the leverage to enforce changes to IoT’s materiality.

1 INTRODUCTION

The physical world, as we know it, merges more and

more with its digital counterpart. The Internet of

Things (IoT) is driving this transformation we

observe, by raising data collection to an

unprecedented level. Emerging data that might

drastically change the work of people in many

organizations.

The International Data Corporation (IDC)

forecasts the number of Internet-connected devices

will increase from 30 billion in 2020 to 41.6 billion

by 2025 – excluding general-purpose devices, such as

smartphones, tablets, and PCs. In 2025, IoT devices

will generate 79.4 zettabytes of data, providing

unparalleled insight (MacGillivray & Reinsel, 2019).

How IoT affects adopting organizations is still

opaque. With this high-paced development in mind,

one could perceive a sense of urgency, seeing the

present unfamiliarity with IoT’s socio-technical

implications.

On the one hand, technological advancements,

potential applications, and estimated economic

growth through IoT are extensively discussed in

journal articles, business reports, and news media. On

https://orcid.org/0000-0002-3565-0623

https://orcid.org/0000-0001-5635-1588

the other hand, sufficient knowledge about the impact

on people that work with IoT in organizations seems

hitherto to be missing from the academic debate.

Literature bodies either focus on socio-technical

systems, which incorporate a general perspective on

people and technology in organizations, or they focus

on IoT, addressing the technical design and

economics (Madakam, Ramaswamy, & Tripathi,

2015). Separately, these streams exist abundantly,

but, as to the authors’ knowledge, research combining

both perspectives has yet to be published, thus

offering many opportunities for research questions at

all levels to be explored and tested in today’s business

environment.

Gaining a holistic view of the interaction between

technology and people is critical. Chua and Lam

(2005) argue for the fatality to consider the technical

aspects unilaterally. Even though technical

requirements can be met, the appropriation can be

unsuccessful if the technology does not receive

enough ongoing support from its users. Hence, it is

crucial for the effective use of technologies to

incorporate social aspects. This standpoint is shared

by Boos and Grote (2012) as well as Shin (2014),

saying that understanding and considering the

Boonstra, A. and Wiktor-Steffens, D.

The Socio-technical Impact of the Internet of Things: An Exploratory Mixed Methods Research.

DOI: 10.5220/0009327101770188

In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security (IoTBDS 2020), pages 177-188

ISBN: 978-989-758-426-8

177

interplay between technology and people is a pivotal

success factor. In effect, failing to address both

aspects in a balanced fashion leads to higher risks that

technology implementations will not be used to their

full potential or even remain without success.

IoT’s social impact should not be left ignored

(Shin & Jung, 2012). The current and little

interdisciplinary academic discussion leaves us with

the realization that some aspects of the socio-

technical lens are not sufficiently explored and that

the contemporary relevance of the topic allows for

novel research questions. Using Leonardi’s Socio-

Technical System Model (2012) as a lens, this study

aims to explore how IoT in the technical subsystem

impacts the social subsystem and vice versa (see

Figure 1). Through the presented model, the

preliminary research question can be split into the

following three research questions (RQ): RQ I

addresses IoT’s impact on the social subsystem, RQ

II assesses the impact of changes in the social system

on intentionality, and RQ III pays attention to the

impact of changes in the social system on IoT’s

materiality (compare Figure 1).

2 LITERATURE REVIEW

Several academics agree that IoT is a collection of

devices equipped with numerous types of sensors that

are constantly connected via the Internet. These

interconnected things collect and deliver precise data

from the physical world without direct human

involvement (e.g., Al-Fuqaha, Guizani, Mohammadi,

Aledhari, and Ayyash (2015); Atzori, Iera, and

Morabito (2017); Madakam et al. (2015).

The network of connected devices that

continuously measures the environment with sensors

generates large amounts of data. Enabled by a

semantic layer, the technology transforms data into

relevant information that holds meaning to the user.

The automated collection of data in an unparalleled

quantity constitutes the central affordance of IoT

(Ouaddah, Mousannif, Abou Elkalam, & Ait

Ouahman, 2017; Shi, Li, Zhu, & Ning, 2018).

Affordances are all qualities of a technology that

define its possible uses (Majchrzak & Markus, 2013;

Volkoff & Strong, 2013).

Socio-technical system theory helps to analyze

the organizational interplay between technology and

people, especially how the affordance influences

employees in their work environment. Because of the

reciprocal relationship, socio-technical systems take

both the technological and the social perspective into

consideration. Paul Leonardi’s (2012) model is

especially suitable to observe IoT through the lens of

the socio-technical system theory, as it incorporates

the mutual shaping of technology and people and has

not yet been used to analyze IoT. Moreover, it

distinguishes the interrelatedness of the two

perspectives, namely the social subsystem and the

technical subsystem, in three main impacts; these

serve as the basis to formulate the research questions

(see center arrows in Figure 1).

Figure 1: Socio-Technical System Model (Leonardi, 2012).

Leonardi describes a socio-technical system

consisting of a social subsystem and a technical

subsystem (Figure 1). The social subsystem is

essentially a coherent whole that is formed by

existing interrelationships between individuals within

the organization. Generally, people that interact and

work together build a stand-alone social subsystem

within a group or organization. Defined by the

relationships among the people in the social

subsystem, it is influenced by various aspects like

their abilities, power, assumptions and beliefs,

knowledge, experience with and expectations about

technology, as well as its affordances and constraints

(Orlikowski & Gash, 1994).

The technical subsystem includes both the person

and the artifact. The artifact refers to the technology

as a non-human entity. It has its own materiality,

which is the set-up of physical and digital materials

in particular forms that matter to the user. Those

properties of the artifact do not change, by

themselves, across differences in time and context.

Materiality produces the affordances and constraints

of an artifact that are activated through intentionality

(Leonardi & Barley, 2008).

Whereas artifacts have materiality, persons have

intentionality, which expresses the desired outcome

they have in mind. When users approach artifacts

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178

with materiality, they form particular goals or human

agency. The coordination of multiple human agencies

constitutes a social agency (Leonardi, 2012).

A central aspect of the interconnectedness is that

the users’ intentionality is subject to influencing

factors in the social subsystem. To a large extent,

users formulate their goals and decide in which ways

they will proceed using the technology based on their

perception of its possibilities and limitations. Their

opinion about the technology is a result of the

sensemaking process in the social subsystem. Thus,

users have considerable influence on the degree to

which technology affects their working environment.

To accomplish their intentions, people choose to

use certain elements of the artifact’s materiality at a

given time. The artifact’s materiality acts by users

activating material agency, meaning they approach

materiality with intentions. Hence, while materiality

refers to properties of the object, material agency

refers to the way the object acts when humans

provoke it. A clear distinction is necessary because

materiality is rather stable over time, but material

agency frequently changes, depending on who is

making sense of the materiality and which intentions

this user has. The social agency is partly shaped in

response to the material agency of all users in the

system and how they perceive each other’s intentions.

The material agency would not exist without users’

intentions to use the technology (Leonardi, 2012;

Wagner, Newell, & Piccoli, 2010).

The social and material agencies are unlike each

other but create a mutual new structure when both

come together. Leonardi (2012) uses the term

imbrication to explain this occurrence; the agencies

become imbricated in the space of practice. In other

words, individuals make use of the technology. As a

matter of fact, imbrication is the actualization of the

technologies’ affordances. The study shows that

specific imbrications cause changes in the social

subsystem, such as in roles, status, and hierarchies.

Transformations in these abstract formulations can

form new future patterns of imbrication. Vice versa,

these transformations can cause changes in the

rtifact’s materiality and person’s intentionality.

3 METHODOLOGY

Due to the underrepresentation of a holistic approach

in research, this study is constructed using a

qualitatively driven mixed methods design (Saunders,

Lewis, & Thornhill, 2016). First, semi-structured

expert interviews were conducted to identify and

explain emerging phenomena. Based on the identified

core aspects that emerged from the in-depth

interviews, a questionnaire was designed and carried

out to allow increased generalizability and

triangulation. The quantitative results from the survey

helped to create a clearer picture of the qualitative

interviews. The qualitative analysis is dominant due

to the need to create an initial understanding and

provide the basis to generate theory. Results were

compared across both the qualitative and quantitative

data collection. Ultimately, generated insight was

superimposed onto the presented model to derive

conclusions (Eisenhardt, 1989).

3.1 Qualitative Part

For this study, the primary method is qualitative.

Semi-structured expert interviews with limited access

to documents were used to collect data as the topic’s

novelty gave reason to assume a lack of experience

with IoT of most organizations and employees. A

vastly distributed sample led to conducting interviews

via phone with an average duration of 50 minutes.

Each interviewee agreed to audio recordings for

subsequent transcripts. One pretest call with feedback

was performed to identify logical inconsistencies and

unclear formulations. After transcribing all

interviews, the computer program Atlas.ti was used to

create and categorize codes. The coding of transcript

included a balance of deductive and inductive codes

to ensure theoretical and emerging aspects. The data

was analyzed across experts.

The sample consists of 21 experts from business

and academia from the US, China, India, Germany,

the Netherlands, UK, Spain, and Sweden. All selected

participants are responsible for, or knowledgeable of,

the implementation and development of IoT. 90% are

in leading positions or have notable decision-making

power and staff responsibility. The represented

organizations fall into the following roles: (1) IoT

solution provider with additional services for their

platforms and other use cases (43%), (2) management

consulting firms with dedicated IoT practices (38%),

(3) independent IoT research institutions (10%), (4)

research and consulting firms (5%), and (5) internal

IoT research and development (5%).

3.2 Quantitative Part

An online survey is the quantitative and thus

secondary part of the mixed methods approach. It was

designed to test the reliability of the expert interviews

and to provide more generalizable results. The

questionnaire was based on the research questions

and propositions derived from qualitative expert

The Socio-technical Impact of the Internet of Things: An Exploratory Mixed Methods Research

179

interviews. Respondents (R) were mainly asked to

evaluate eleven statements along the research

questions on a 5-item Likert scale ranging from

“strongly agree” to “strongly disagree.” The results

were used to support the insight generated through the

primary interviews.

The response rate was 17.6% (N = 168). After

scrutinizing for incomplete responses, unsuitable

professions, and multivariate outliers, a sample of N

= 123 remained. On average, respondents had eight

years of experience in the field of IoT (M = 8.03, SD

= 6.73). The majority reported being direct users of

IoT applications (64.2%). Only 3.3% work for the

government. Most participants work at organizations

with over 10,000 employees (33.3%), with 1-4

employees (29.3%), and with 50-499 employees

(18.7%)

. Their location is in Europe (37,4%), North

America (27.6%), Asia (24.4%), South America

(4.1%), the Middle East (4.1%), and in Oceania

(2.4%).

4 RESULTS

4.1 Impact on Social Subsystem (RQ I)

The newly gained access to massive amounts of data

supposedly is the main driver for changes in the social

subsystem. The expert interviews confirm the central

affordance of IoT: “All of a sudden, you are able to

read an incredible amount of sensor data, that really

triggers the change” (R4, personal communication,

2018-04-30). In addition, the interviews suggest that

four dominant aspects in the social subsystem are

affected by the abovementioned information flow.

Each aspect will be considered below.

4.1.1 Transformed Role

Transformed roles are evident since aspects related to

changes in employees’ roles are touched upon during

all primary interviews. Besides, the secondary survey

demonstrates a high degree of agreement among the

sample, as Figure 2 shows:

Figure 2: IoT Influences Roles in the Social System.

6.5% with 500-999 employees, 6.5% with 5000-9999

employees, and 5.7% with 1000-4999 employees.

By influencing tasks and their required skills, IoT

influences employees’ roles in many facets. The

experts imply that these shifts in demands associated

with a particular role are likely to be disrupted in the

age of IoT. Therefore, IoT questions the current roles

of employees: “Jobs […]; job profiles […and],

qualifications are changing. Also important is the

question, which work I will still be able to perform

with my current set of qualifications and also which

qualifications are required due to new technologies.

That is a point that surely influences the whole social

system” (R9, personal communication, 2018-05-08).

After categorizing all codes contributing to the

transformation of roles, the following four prevail. (1)

Gravitation towards an intellectual and digital role:

“It will be essential whether they have an IT

background or whether they manage to obtain that

crucial knowledge” (R16, personal communication,

2018-11-21). (2) Increased focus on core tasks: “Yet,

you could provide a way to get rid of some of that

administrative, nonvalue-added activities and let

them do what they are trained to do” (R4, personal

communication, 2018-04-30). (3) Upskilling

workforce: “[IoT] demands employees to educate

themselves further, both the textual and technical

related competencies.” (R3, personal

communication, 2018-05-02). Lastly, (4)

redeployment of workforce into new role: “These

employees who used to work as crane operators will

be promoted to the supervisory level” (R16, personal

communication, 2018-11-21).

4.1.2 Flattened Hierarchy

The primary interviews suggest that IoT

implementations influence the hierarchy as a part of

the social subsystem. More specifically, interviews

imply the potential to flatten hierarchies, e.g.: “When

you deploy technologies like IoT that can have a side

effect that flattens organizational hierarchies” (R12,

personal communication, 2018-05-01). The

secondary survey supports this (see Figure 3) with

12% strongly agreeing and 41% somewhat agreeing.

Factors causing flattened hierarchies were grouped

into three categories: (1) Gained accessibility of

information: E.g. “[…] information come together at

the top of the hierarchy. IoT could make it possible to

distribute that information more, and that could

result in a flatter hierarchy” (R8, personal

communication, 2018-05-04). (2) Altering power

relations: “[…] the gap between hierarchical levels

could become smaller” (R8, personal

Strongly

agree

Somewhat

agree

Neither agree

nor disagree

Somewhat

disagree

Strongly

disagree

Percent

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communication, 2018-05-04). And (3) the

restructuring through redundancy: […I]nformation

processing jobs go away. Simply because you have

technology that learns those jobs” (R12, personal

communication, 2018-05-01).

Figure 3: IoT Flattens Hierarchies.

4.1.3 Decreased Privacy

An IoT-enabled working environment almost

inevitably observes employees and collects data

about them, which are potentially accessible to others.

Losing privacy means being uninformed about

ongoing observations and lacking the freedom to

reject these (Sarwar, Yongchareon, & Yu, 2018).

Decreased privacy raises concerns that seem to

impact the social subsystem strongly. Emerging

concerns are also widely recognized in the provided

documents and the secondary survey (Figure 4) with

37% strongly agreeing and 38% somewhat agreeing.

Figure 4: The Extensive Data Collection through IoT

Triggers Privacy Concerns among Employees.

Privacy is potentially a trade-off for improved

organizational efficiency, as organizations

incorporate more monitoring to increase their

measurable profitability. Hence, this may be an

indicator that profit-driven organizations will keep

touching upon these concerns: “Because surveillance

[…] is an outgrowth of efficiency and bureaucratic

evolution. The highly monitored nature of the world

that we are beginning to experience is a natural

outgrowth of a variety of socio-technical trends” (R6,

personal communication, 2018-05-02).

The protection of privacy is a requirement for

employees to develop trust, enabling them to harness

IoT’s benefits: “[…] you start with trust by doing

these proper [privacy] reviews and implementing

proper safeguards and then after that, hopefully, the

employees do see the benefits and the value” (R4,

personal communication, 2018-04-30). Policies, e.g.,

the European General Data Protection Regulation

(GDPR), limit the neglect of privacy in the course of

efficiency gains. Though, there are significant

national differences, having a decisive influence on

risks related to privacy: “It may be totally different in

Germany, Austria, Switzerland, and Europe,

compared to the United States, China, and Africa”

(R1, personal communication, 2018-05-04).

Additionally, the presence and power of labor unions

representing the workforce diverge across different

nations.

Lastly, one expert mentions the possible

occurrence of splits in the social subsystem, reporting

that “in the worst case, it will cleave the social system

in two.” The trigger of faultlines may be the

employees’ tolerance towards privacy concerns: “On

one hand [there are] people who don’t ‘care’ and

make maximum use of IoT and take advantage of it.

They also agree to pay the price in form of data. […]

On the other hand, there will be people who try to

evade IoT completely” (R10, personal

communication, 2018-05-08). A Faultline is a gap

that splits a group into at least two subgroups, caused

by certain attributes that each subgroup identifies

with (Meyer & Schermuly, 2012). The secondary

survey results support this phenomenon as the

majority of respondents (68.3%) agree that “IoT

causes contrasting opinions about privacy, which

create faultlines.”

4.1.4 Increased Transparency

Based on the experts, transparency is about the

continuous accessibility of performance

measurement. Integrated sensors enable

organizations to monitor employees’ actions and

decisions constantly. For instance, a “[…] truck

driver who sits in such a connected truck, of course,

is fully monitorable and it is always clear where he

is, when he drives, when does he stand, how much fuel

does he consume, how much do his colleagues

consume, and so on” (R1, personal communication,

2018-05-04).

IoT-enabled performance measurements do not

seem to resonate positively with employees:

“Actually, I hardly know any worker who is interested

in his work being measured” (R14, personal

communication, 2018-05-07). The primary

interviews and secondary survey support that

increasing transparency seems to create a threat of

losing one’s autonomy (see Figure 5) with 13.0%

strongly agreeing and 41.5% somewhat agreeing.

Strongly

agree

Somewhat

agree

Neither agree

nor disagree

Somewhat

disagree

Strongly

disagree

Percent

Strongly

agree

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agree

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nor disagree

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disagree

Strongly

disagree

Percent

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181

Figure 5: IoT-Enabled Performance Metrics About

Employees Decrease Their Perceived Autonomy.

The interviews reveal that two factors can

influence the perception of increased transparency

positively. Firstly, educating users regarding the

provided advantages that come with more

transparency. A reoccurring example from the

interviews is improved safety, e.g., through IoT-

enabled trucks preventing accidents. Secondly,

informing users about how gathered insight is utilized

might change the perception from negative to

positive: “When it is ambiguous, when they do not

know what they are being monitored for, it is

definitely negative. But, if, if the company is telling

them upfront that they are not trying to pin-point a

particular driver or put a penalty on a particular

person, but, overall trends which all the trucks

combined generate and hence they know this or that

route is better, where they should refuel the vehicles.

All these kinds of definitions come out, and they know

that they will not be individually monitored, then, I

think, it becomes positive” (R17, personal

communication, 2019-02-12).

4.2 Impact on Intentionality (RQ II)

The gathered interview data show that employees’

intentionality conceivably alters due to changes in the

social subsystem: “Yes, there will probably be

behavioral changes. I would be honestly surprised if

it did not […]” (R1, personal communication, 2018-

05-04).

4.2.1 Expression of Changed Intentionality

The primary interview data suggest that changes

related to intentionality appear to surface in three

forms of negative behavior: (1) Reduced willingness

to use IoT: “[…] I would say, yes, […] he either

avoids using or avoids using parts of the technology

[…]” (R9, personal communication, 2018-05-08). (2)

Application of workarounds: “[T]he user could try to

use a workaround” (R15, personal communication,

2018-04-29). (3) Resistance towards the

implementation of IoT solutions. Especially privacy

concerns were mentioned as a possible cause for

threats that result in resistance: “You feel like big

brother is watching. I think privacy is one of the

elements that would drive the resistance and

especially in industrial Europe because some of the

other regions are far less concerned about privacy”

(R4, personal communication, 2018-04-30).

4.2.2 Causes for Changed Intentionality

The secondary survey results support that the four

previously introduced social aspects in chapter 4.1

might impact intentionality. The survey respondents

ranked the four social aspects in order of their

potential to provoke resistant behavior: first, privacy

(M = 1.62, SD = .944), second, transparency (M =

2.69, SD = 1.094), third, hierarchy (M = 2.81, SD =

.977), and fourth, role (M = 3.09, SD = 1.116).

While the social aspects constitute the major

influence of IoT on the social subsystem, the primary

interviews infer these changes to translate into two

factors that impact employees’ intentionality: (1)

Perceived threats. Employees do not necessarily fear

the technology but what comes along with it. They

“[…] can be quite fearsome […]. That doesn’t

necessarily have to be with regards to the quality of

the hardware and the software […] but it can be seen

as a certain threat to their job, changes in the

hierarchy, and that creates that he might not adopt

new technologies or refuses them” (R8, personal

communication, 2018-05-04). Therefore, it appears

that changes in the social subsystem can cause threats,

potentially altering employees’ intentionality. These

findings are backed by the survey where most

respondents (58.4%) agree that perceived threats

resulting from changes in the social system alter

users’ intention to deploy IoT solutions (Figure 6).

Figure 6: Perceived Threats Resulting from Changes in the

Social System Alter Users’ Intention to Deploy IoT

Solutions.

(2) Perceived Opportunities. It seems to be crucial

whether employees are aware of their individual

benefits. Therefore, employees would, e.g., neglect

the technology when the added value is invisible or

unclear to them. The individual benefits should

clearly outweigh the costs (represented by the

potential threat): “If they see value in what’s on offer,

then they’ll become comfortable with it over time;

Strongly agree Somewhat agree Neither agree

nor disagree

Somewhat

disagree

Strongly

disagree

Percent

Strongly

agree

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agree

Neither agree

nor disagree

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They need to see the benefits, […] it’s a trade-off,

right? It’s about how you articulate the value” (R12,

personal communication, 2018-05-01).

The interviews suggest that organizations already

try to avert unfavorable intentionality by involving

employees right from the start. Thus, in advance to

the implementation, perceived threats and

employees’ awareness of individual benefits are

addressed early in the process. Organizations

consider both the human perspective as well as

economic efficiency to be crucial for proper IoT

implementations: “Companies realized that they

must make it right in the beginning to avoid

unpredictable behavior of employees and to avoid

wasting money” (R12, personal communication,

2018-05-01).

4.3 Impact on IoT’s Materiality

(RQ III)

The interviews imply materiality to be less

influenceable than intentionality once IoT is

introduced. The experts’ tendency is that

organizations do not consider adjusting the

materiality due to changes in the social subsystem

once the technical integration is completed:

“Changing the technology, I consider difficult,

changing the use of technology, I consider absolutely

feasible.” (R9, personal communication, 2018-05-

08). Substantial adjustments to IoT’s materiality are

unlikely for three reasons:

(1) Exploitation of performance advantages. The

expert states that “if the output is right, the company

will not change the technology, even if the employee

is dissatisfied or there are other negative effects in the

social system.” IoT solutions will remain the same

unless effects in the social subsystem significantly

impair the performance. E.g. “[…] if the output is not

right, if the employee refuses or develops a

workaround that is not legit anymore, then the

company would be forced to adjust” (R15, personal

communication, 2018-04-29).

(2) The necessity to stay competitive is driving the

application of IoT solutions: “From a corporate

perspective, dealing with IoT is relevant to

competition.” The advantages arise, for instance,

from the following: “If I am faster in the future, if I

reduce machine errors if I can predict downtimes of

the machines and thus prevent them. It's important for

the company to do that” (R3, personal

communication, 2018-05-02). Therefore, adjustments

of the materiality, which are limiting the potential

exploitation of the capabilities may be improbable.

Especially, as IoT is a means to become future proof:

“Personally, I don’t think that these social aspects

can change the architecture. […] Because the

company that decided in favor of the IoT solution. I

mean, they are investing in the future” (R16, personal

communication, 2018-11-21). Evaluating IoT as an

essential element on the digital agenda is also

reflected by the survey results: 46% of the

respondents think that IoT’s importance for firm

performance is somewhat above average, and 39.8%

even think it is far above average.

(3) Problem to reverse the integration of

technological elements of IoT. On the one hand, there

is the view that adjustments are out of the question

since organizations have a set of motives for not

adjusting IoT’s materiality and, therefore, the

disadvantages prevail. 14% of the experts argue that

one cannot just make technology disappear: “It

cannot be ruled out that the properties of IoT

solutions will be changed, but basically, of course, it

is usually the case that once a technology is

integrated, it is difficult to restrain it again” (R1,

personal communication, 2018-05-04). On the other

hand, however, experts emphasize the general

magnitude of the social aspects related to IoT. Their

responses to the latter are all similar to the statement:

“For me, that means that I personally would even put

the social aspects at the heart of an IoT development”

(R15, personal communication, 2018-04-29). Only

10% of the experts can envision organizations

adjusting IoT’s materiality: “I guess, in any scenario

[…] you have to be accommodating to two things, to

your bottom-line, but also to your staff and their

feelings. So, you have to take both into account […].

I can see that there are changes made” (R13,

personal communication, 2018-05-10). In line with

this statement, the secondary survey shows that

46.9% disagreed with the statement that the technical

design of IoT solutions remains unchanged despite

changes in the social system, while only 33.6%

agreed. A potential explanation for this discrepancy

might lie in the stages in time in which social aspects

are incorporated. Organizations address social issues

already before and during the implementation process

of IoT, whereas “changing” materiality implies a

post-implementation stage. The experts unanimously

describe that “[…] especially in the field of IoT […it]

is too costly if you first develop something and then

check the feasibility or user acceptance, etc” (R15,

personal communication, 2018-04-29).

Whether or not organizations adjust the

technology’s materiality depends on a moderating

effect of employees’ leverage over the organization.

Analyzing the interview data, several leverage-

creating attributes were accumulated: expertise,

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flexibility in mobility, hierarchical level, and unique

skill set. Vice versa, the employer has attributes to

exert leverage over the respective employee with

access to qualified workforce and flexibility in

mobility. Survey respondents generally agree that

employees who possess power within the

organization have a noteworthy influence on IoT’s

materiality (69% agree, and 16.9% disagree). Given

that both employees and employers appear to have

leverage, they may have to be weighed against each

other to assess whether potential changes in IoT’s

materiality need to be addressed or can be suppressed.

5 DISCUSSION

5.1 Answering the Research Questions

The primary expert interviews and the secondary

survey results help to explore the impacts of IoT

within Leonardi’s Model (2012). The adjusted model

in Figure 7 presents the discussion’s outcomes.

5.1.1 Impact on Social Subsystem (RQ I)

The results suggest the following four ways how IoT

impacts the social subsystem:

(1) Transformed Role. The results suggest that

employees’ roles will change as IoT enables

increasing automation and massive information

enrichment. In line with the literature, the findings

advise that constant education is crucial to

maintaining a valuable role within the organization

(Bresnahan, Brynjolfsson, & Hitt, 2002).

Computerization, primarily driven by data

acquirement via sensors, increases the demand for

highly educated and creatively thinking roles (Frey &

Osborne, 2017). Therefore, employees must be

prepared to adapt their roles faster in an IoT

accelerated environment.

(2) Flattened Hierarchy. This research proposes

that IoT flattens organizational hierarchies when

information is distributed equally and across all

levels. Information is a source of power (Pettigrew,

2016). When information is available at all levels, it

is most-likely to re-allocate decision-making powers

and flatten hierarchical patterns (Dobrajska,

Billinger, & Karim, 2015). Moreover, IoT can be

responsible for technological unemployment

(Brynjolfsson & McAfee, 2014).

(3) Decreased Privacy. Privacy issues have a

substantial impact on employees’ well-being. To a

degree, security matters can justify employees’

location tracking or video surveillance. Generally,

privacy concerns among employees cause decreased

acceptance of IoT solutions. Bélanger and Crossler

(2011) support that privacy concerns reduce the

intention to use information technologies. Their study

implies four dimensions that cause uneasiness of

employees: (1) nontransparent collection of data, (2)

unauthorized secondary use of data, (3) improper

access to data, and (4) errors in data. The results even

reveal that privacy concerns can be considered as a

cause for passive to active forms of resistance

(Chang, Liu, & Lin, 2015). Also, the occurrence of

faultlines provoked by privacy is possible, separating

the social subsystem into subgroups ranging from

being concerned with privacy issues to not being

concerned with them at all. These subgroups may

increasingly work against each other, harming the

organization (Meyer & Schermuly, 2012).

(4) Increased Transparency. Research of Levy

(2018) about truck drivers in IoT-supported vehicles

comes to a similar conclusion that transparent

performance metrics likely negatively affect job

satisfaction. The underlying reasons may be the felt

loss of leeway and decision freedom. Continuous

controllability through sensors takes away

employees’ autonomy. As a consequence, diminished

autonomy leads to lower job satisfaction and can

cause burnout symptoms (Arches, 1991). On the

contrary, it was also reported that increased

transparency generates data that can facilitate fairness

by providing additional proof, e.g., in the case of

truck accidents.

5.1.2 Impact on Intentionality (RQ II)

The abovementioned four social aspects identified in

this research, do not directly impact users’

intentionality. Instead, transformed roles, flattened

hierarchies, decreased privacy, and increased

transparency, constitute changes as these new

circumstances diverge from the incumbent status quo.

These changes cause perceived threats that ultimately

seem to result in alterations in intentionality. This is

in line with Lapointe and Rivard (2005), describing

that it is the object of change that interacts with initial

conditions and not the technology or a specific aspect

in the social system itself that triggers perceived

threats.

In the case of technology adaptation, perceived

opportunities lead to the exploitation of a given

technology or even the exploration to innovate. On

the contrary, perceived threats cause an exploration to

revert or even avoidance of the technology (Bala &

Venkatesh, 2016). This interplay is in sync with the

change equation, describing that the benefit of and

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need for change must outweigh the costs of change

(Beckhard, 1975; Dannemiller & Jacobs, 2016). This

notion is also explained by the equity theory in social

sciences, describing the assessment of gain and loss

of changes (Joshi, 1991).

Hence, perceived opportunities compromise the

moderating effect of perceived threats on

intentionality (see Figure 7). The study suggests that

mainly, the four identified social aspects cause threats

that trigger a range of reactions away from neutral

behavior to passive or active resistance (van

Offenbeek, Boonstra, & Seo, 2013). Therefore,

organizations must ensure to incorporate these social

aspects to reinforce successful IoT implementations

(Bersin, Mariani, & Monahan, 2016; Jones, Derasse,

Chitale, & Negri, 2016).

Figure 7: Adjusted Socio-Technical System Model.

5.1.3 Impact on IoT’s Materiality (RQ III)

The conducted research infers that it is indistinct

whether organizations adjust IoT’s materiality in

response to changes in the social subsystem. On one

side, the findings show that the immense investments

of IoT implementations hamper organizations to

leave their planned course of action, even though

people in the social subsystem express their aversion.

Exploring why organizations do not reverse

technological implementations while facing

substantial headwinds, Keil, Truex, and Mixon

(1995) propose two main factors, the level of costs

and the level of project completion. Both aspects

determine the influenceability of materiality. The

higher the costs and further the progress of

implementation, the less likely adjustments appear. In

their consideration, costs were even more significant

than project progress.

In fact, multiple sources confirm that IoT is

considered to be exceptionally cost-intensive

compared to other technologies. Thus, organizations

are compelled to assess each challenge thoroughly

and plan, design, and deploy all aspects with the

highest diligence (Lee & Lee, 2015; Niyato, Lu,

Wang, Kim, & Han, 2016). The necessity to “do it

right the first time” (R12, personal communication,

2018-05-01) reflects the necessity of great efforts in

change management during the implementation. It is

too costly to adjust materiality because of adverse

responses in hindsight. Nevertheless, the survey

results also point at a perceived high likelihood that

organizations do consider adjustments of IoT’s

technical design due to social changes. However,

some employees have distinctive qualities, such as a

unique skill set, which creates an overall exception to

enforcing adjustments of materiality. Their leverage

makes organizations incapable of ignoring their

criticism unless they exert leverage themselves

through, e.g., their access to a large selection of

qualified applicants. The forces of both parties appear

to have a moderating effect on how changes in the

social subsystem influence the materiality in the

technical subsystem (compare Figure 7).

5.2 Theoretical Contributions

The study is the first to bring a degree of clarity to the

way IoT affects the employee in the social subsystem

by applying the theory of Leonardi (2012). The

reported main effects open the hitherto black box of

social implications provoked by IoT (Shin & Jung,

2012). Moreover, it is the first study to explore how

effects in the social subsystem influence users’

intentionality.

The findings confirm the idea that it is an object

of resistance instead of specific artifacts or elements

in the socio-technical system that creates perceived

threats. Employees assess the costs and benefits of the

disruption of their initial state for an upcoming

change. A prevailing perception of a threat will

provoke resistant behaviors, spanning from

avoidance, over applying workarounds, to active

resistance (Lapointe & Rivard, 2005).

The study addressed the influence of social effects

on IoT’s materiality, unveiling indistinct results.

There is ambiguous evidence whether organizations

would adjust the technology due to changes in the

social subsystem. Several incentives keep

organizations from adjusting their IoT solutions.

Among them are, for example, the tremendous costs

that come along with an IoT implementation and the

necessity to incorporate IoT use cases to stay

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competitive (Keil et al., 1995). However, the results

show a moderating effect of both employees’ and

employers’ leverage over each other. Consequently, a

moderator was added to the model (Figure 7) to imply

the evaluation of, for instance, the employees’

leverage due to a unique skill set versus the

employers’ extensive access to skilled people.

5.3 Practical Implications

Derived from IoT expert interviews, the

recommendations’ suitability is neither limited to IoT

nor should they be understood as exhaustive. First,

the results determine the four core aspects that ought

to be monitored with close attention on the journey to

IoT appropriation. Ideally, before, during, and after

the implementation to detect even slight irregularities

over time and avoid perceived threats.

Second, when perceived threats endanger users’

intentionality, the study suggests presenting

perceived opportunities to be an effective

counteraction. Accordingly, benefits for involved

users should be put at the center of the discussion.

5.4 Research Limitations

Due to IoT’s arguable novelty and (so far) limited

distribution, expert interviews were preferred over

interviews with directly affected employees. While

this method allowed comprehensive insight into IoT

in the socio-technical system, the observations are

more abstract. The majority of survey respondents,

however, reported already being direct users of IoT.

The in-depth analysis of mechanisms had to be

neglected due to the variety of specialist knowledge

among the interviewees. Gathering various examples

from different fields was, nevertheless, more valuable

for a universal understanding.

40% of interviewees are German, which can bias

the results because of the geographical commonalities

such as strong privacy regulations and labor unions.

The supplementary survey was distributed via

professional networks. While reducing the chance of

contextual misunderstandings, the approach inherits a

risk of selection bias and false individual portrayal.

Although IoT can be explicitly described, its use

cases often blur with other technologies. Since it is

the infrastructure and enabler for several themes, it is

challenging to match cause and effect to a distinctive

technology. Therefore, the reader ought to be

reminded that IoT operates among other technologies

that influence findings reciprocally.

5.5 Suggestions for Further Research

This study contributes to laying the foundation for

future research exploring detailed mechanisms

through, e.g., a specific IoT application in an

empirical setting. Thus, the presented adapted model

(Figure 7) can be utilized for case studies to verify its

reliability.

Furthermore, in the light of practical relevance,

future research should investigate strategies to

counteract disturbances in the socio-technical system

related to IoT.

6 CONCLUSIONS

Numerous scholars and reports (e.g., Shi et al., 2018;

Bhatia et al., 2017; Bersin et al., 2016) emphasize the

magnitude of IoT as it is a driving force for the digital

revolution. Looking at the current IoT research, the

predominant focus on technology and business issues

becomes apparent. Consequently, the social

component does not receive the attention it deserves,

considering its importance after implementation.

At question is in what way IoT in the

organization’s technical subsystem impacts the social

subsystem and vice versa. The analysis shows that

access to an unprecedented amount of data affects

some aspects of the social subsystem more than

others. These are transformed roles, flattened

hierarchies, decreased privacy, and increased

transparency. The findings indicate two moderating

effects in the socio-technical system. Firstly,

alterations in the social subsystem lead to perceived

threats that negatively change users’ intention to use

IoT. However, perceived opportunities through IoT

have a moderating effect as they weaken this

relationship. Secondly, alterations in the social

subsystem may have little impact on IoT’s materiality

due to, e.g., the necessity to remain competitive.

Nonetheless, the relationship between social

subsystem and materiality can be affected by the

respective individuals and their unique

characteristics; employees with arguably important

attributes have leverage to reinforce the relationship,

leading to more technical adjustments. Employers’

leverage, however, can weaken the relationship.

This study aims to raise awareness of the social

impact of IoT, in the hope that future research will

shift its focus beyond technological and economic

opportunities to a more human-oriented approach.

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REFERENCES

Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari,

M., & Ayyash, M. (2015). Internet of Things: A Survey

on Enabling Technologies, Protocols, and Applications.

IEEE Communications Surveys & Tutorials, 17(4),

2347–2376. https://doi.org/10.1109/COMST.2015.

2444095

Arches, J. (1991). Social Structure, Burnout, and Job

Satisfaction. Social Work, 202–206.

https://doi.org/10.1093/sw/36.3.202

Atzori, L., Iera, A., & Morabito, G. (2017). Understanding

the Internet of Things: Definition, potentials, and

societal role of a fast evolving paradigm. Ad Hoc

Networks, 56, 122–140. https://doi.org/10.1016/j.

adhoc.2016.12.004

Bala, H., & Venkatesh, V. (2016). Adaptation to

Information Technology: A Holistic Nomological

Network from Implementation to Job Outcomes.

Management Science, 62(1), 156-179.

https://doi.org/10.1287/mnsc.2014.2111

Beckhard, R. (1975). Strategies for large system change.

Sloan Management Review, 16(2), 43–55.

Bélanger, F., & Crossler, R. (2011). Privacy in the Digital

Age: A Review of Information Privacy Research in

Information Systems. MIS Quarterly, 35(4), 1017–

1042. https://doi.org/10.2307/41409971

Bersin, J., Mariani, J., & Monahan, K. (2016). Will IoT

Technology Bring us the Quantified Employee?

Retrieved from Deloitte website:

https://www2.deloitte.com/content/dam/insights/us/art

icles/people-analytics-iot-human-resources/ER_3104_

IoT_Human-capital_vFINAL.pdf

Bhatia, A., Hunke, N., Kalra, N., Rüßmann, M., Schmieg,

F., & Yusuf, Z. (2017). Winning in IoT: It's all about

the business processes. Boston, United States.

Retrieved from The Boston Consulting Group (BCG)

website: http://img-stg.bcg.com/BCG-Winning-IoT-

Jan-2017_tcm9-161204.pdf

Boos, D., & Grote, G. (2012). Designing Controllable

Accountabilities of Future Internet of Things

Applications. Scandinavian Journal of Information

Systems, 24(1), 1–28. Retrieved from

http://aisel.aisnet.org/sjis/vol24/iss1/1

Bresnahan, T. F., Brynjolfsson, E., & Hitt, L. M. (2002).

Information Technology, Workplace Organization, and

the Demand for Skilled Labor: Firm-Level Evidence.

The Quarterly Journal of Economics, 117(1), 339–376.

https://doi.org/10.1162/003355302753399526

Brynjolfsson, E., & McAfee, A. (2014). The Second

Machine Age: Work, Progress, and Prosperity in a

Time of Brilliant Technologies (First Edition). New

York: W. W. Norton & Company.

Chang, S. E., Liu, A. Y., & Lin, S. (2015). Exploring

privacy and trust for employee monitoring. Industrial

Management & Data Systems, 115(1), 88–106.

https://doi.org/10.1108/IMDS-07-2014-0197

Chua, A., & Lam, W. (2005). Why KM projects fail: A

multi ‐ case analysis. Journal of Knowledge

Management, 9(3), 6–17. https://doi.org/10.1108/

13673270510602737

Dannemiller, K. D., & Jacobs, R. W. (2016). Changing the

Way Organizations Change: A Revolution of Common

Sense. The Journal of Applied Behavioral Science,

28(4), 480–498. https://doi.org/10.1177/

0021886392284003

Dobrajska, M., Billinger, S., & Karim, S. (2015).

Delegation Within Hierarchies: How Information

Processing and Knowledge Characteristics Influence

the Allocation of Formal and Real Decision Authority.

Organization Science, 26(3), 687–704.

https://doi.org/10.1287/orsc.2014.0954

Eisenhardt, K. M. (1989). Building Theories from Case

Study Research. Academy of Management Review,

14(4), 532–550. https://doi.org/10.5465/AMR.1989.

4308385

Frey, C. B., & Osborne, M. A. (2017). The future of

employment: How susceptible are jobs to

computerisation? Technological Forecasting and

Social Change, 114, 254–280.

https://doi.org/10.1016/j.techfore.2016.08.019

Jones, A., Derasse, A., Chitale, A., & Negri, A. (2016).

Internet of Things: Visualise the Impact. USA.

Joshi, K. (1991). A Model of Users' Perspective on Change:

The Case of Information Systems Technology

Implementation. MIS Quarterly, 15(2), 229–242.

https://doi.org/10.2307/249384

Keil, M., Truex, D. P., & Mixon, R. (1995). The effects of

sunk cost and project completion on information

technology project escalation. IEEE Transactions on

Engineering Management, 42(4), 372–381.

https://doi.org/10.1109/17.482086

Lapointe, & Rivard (2005). A Multilevel Model of

Resistance to Information Technology Implementation.

MIS Quarterly, 29(3), 461–491.

https://doi.org/10.2307/25148692

Lee, I., & Lee, K. (2015). The Internet of Things (IoT):

Applications, investments, and challenges for

enterprises. Business Horizons, 58(4), 431–440.

https://doi.org/10.1016/j.bushor.2015.03.008

Leonardi, P. M. (2012). Materiality, Sociomateriality, and

Socio-Technical Systems: What Do These Terms

Mean? How Are They Different? Do We Need Them?

In P. M. Leonardi, B. A. Nardi, & J. Kallinikos (Eds.),

Materiality and Organizing (pp. 25–48). Oxford

University Press.

Leonardi, P. M., & Barley, S. R. (2008). Materiality and

change: Challenges to building better theory about

technology and organizing. Information and

Organization, 18(3), 159–176.

https://doi.org/10.1016/j.infoandorg.2008.03.001

Levy, K. (2018, January). Data Driven: Truckers and the

New Workplace Surveillance. Berkeley Sociology,

Berkeley, United States. Retrieved from

http://sociology.berkeley.edu/karen-levy-data-driven-

truckers-and-new-workplace-surveillance

MacGillivray, C., & Reinsel, D. (2019). Worldwide Global

DataSphere IoT Device and Data Forecast, 2019–

2023.

The Socio-technical Impact of the Internet of Things: An Exploratory Mixed Methods Research

187

Madakam, S., Ramaswamy, R., & Tripathi, S. (2015).

Internet of Things (IoT): A Literature Review. Journal

of Computer and Communications, 03(05), 164–173.

https://doi.org/10.4236/jcc.2015.35021

Majchrzak, A., & Markus, L. (2013). Technology

Affordances and Constraints in Management

Information Systems. In E. H. Kessler (Ed.),

Encyclopedia of Management Theory (pp. 832–835).

2455 Teller Road, Thousand Oaks, California 91320:

SAGE Publications, Ltd.

Meyer, B., & Schermuly, C. C. (2012). When beliefs are

not enough: Examining the interaction of diversity

faultlines, task motivation, and diversity beliefs on team

performance. European Journal of Work and

Organizational Psychology, 21(3), 456–487.

https://doi.org/10.1080/1359432X.2011.560383

Niyato, D., Lu, X., Wang, P., Kim, D. I., & Han, Z. (2016).

Economics of Internet of Things: An information

market approach. IEEE Wireless Communications,

23(4), 136–145.

https://doi.org/10.1109/MWC.2016.7553037

Orlikowski, W. J., & Gash, D. C. (1994). Technological

frames: Making sense of information technology in

organizations. ACM Transactions on Information

Systems, 12(2), 174–207.

https://doi.org/10.1145/196734.196745

Ouaddah, A., Mousannif, H., Abou Elkalam, A., & Ait

Ouahman, A. (2017). Access control in the Internet of

Things: Big challenges and new opportunities.

Computer Networks, 112, 237–262.

https://doi.org/10.1016/j.comnet.2016.11.007

Pettigrew, A. M. (2016). Information Control as a Power

Resource. Sociology, 6(2), 187–204.

https://doi.org/10.1177/003803857200600202

Sarwar, K., Yongchareon, S., & Yu, J. (2018). A Brief

Survey on IoT Privacy: Taxonomy, Issues and Future

Trends. In Liu X. et al. (eds) Service-Oriented

Computing – ICSOC 2018 Workshops. Springer; Cham.

Saunders, M., Lewis, P., & Thornhill, A. (2016). Research

Methods for Business Students (7th ed.). Harlow,

United Kingdom: Pearson.

Shi, F., Li, Q., Zhu, T., & Ning, H. (2018). A Survey of

Data Semantization in Internet of Things. Sensors

(Basel, Switzerland), 18(1), 313.

https://doi.org/10.3390/s18010313

Shin, D. (2014). A socio-technical framework for Internet-

of-Things design: A human-centered design for the

Internet of Things. Telematics and Informatics, 31(4),

519–531. https://doi.org/10.1016/j.tele.2014.02.003

Shin, D.-H., & Jung, J. (2012). Socio-technical analysis of

Korea's broadband convergence network: Big plans, big

projects, big prospects? Telecommunications Policy,

36(7), 579–593. https://doi.org/10.1016/j.telpol.

2012.03.003

Van Offenbeek, M., Boonstra, A., & Seo, D. (2013).

Towards integrating acceptance and resistance

research: Evidence from a telecare case study.

European Journal of Information Systems, 22(4), 434–

454. https://doi.org/10.1057/ejis.2012.29

Volkoff, O., & Strong, D. M. (2013). Critical Realism and

Affordances: Theorizing IT-Associated Organizational

Change Processes. MIS Quarterly, 37(3), 819–834.

https://doi.org/10.25300/MISQ/2013/37.3.07

Wagner, E., Newell, S., & Piccoli, G. (2010).

Understanding Project Survival in an ES Environment:

A Sociomaterial Practice Perspective. Journal of the

Association for Information Systems, 11(5), 276–297.

https://doi.org/10.17705/1jais.00227

IoTBDS 2020 - 5th International Conference on Internet of Things, Big Data and Security

188