Exploring Dashboards as Socio-technical Artifacts:

Literature Review-based Insights

Aleksandra Revina

1,2 a

Faculty of Economics, Brandenburg University of Applied Sciences, Brandenburg an der Havel, Germany

Faculty of Economics and Management, Technical University of Berlin, 10623, Berlin, Germany

Keywords: Dashboards, Socio-technical Systems, Organizations, Literature Review.

Abstract: In times of Big Data and rapid changes, managers become increasingly reliant on dashboards to make fast

and informed decisions. The advantages of dashboards are evident even at the beginning of Business

Intelligence deployment in organizations. To address the inability of humans to deal with large amounts of

data, dashboards are a typical instrument to represent business-critical information in a comprehensible

manner. However, there are many difficulties in managing the needs of individuals, teams, and organizations

together with the technology in the context of socio-technical systems. While a broad range of technologies

for dashboards’ creation exists, the question remains in how far dashboard solutions consider the needs and

preferences of their primary addressee – human worker. This paper addresses this question by offering a

systematic review of recent literature on dashboards. The study focuses on the end-user perspective and

includes domains, goals, design process and dashboard characteristics, technologies, and impacts of

dashboards’ application in organizations. In conclusion, research gaps and potential directions are

summarized.

1 INTRODUCTION

With the exponential growth of data, organizations

progressively take measures to valorize the enormous

data volumes, using various tools and selecting the

data for particular purposes. Dashboards are widely

implemented to visualize, analyze, interact with, and

present data in various forms. Their application

domains are also limitless (Schöffel, Weibell, &

Schwank, 2018). Thus, dashboards can serve as city

information desks, Business Intelligence tools, and

shop floor boards or provide real-time information on

emergencies.

Organizations either develop dashboards

themselves or use existing software solutions (Aksu,

del-Río-Ortega, Resinas, & Reijers, 2019), the latter

gaining more and more popularity due to their

advanced functionalities and adaptability. The

recognized solutions are Microsoft Power BI,

Tableau, Qlik, and SAP BI, to name a few (Gartner,

2021). As a rule, dashboards serve to support

decision-making, presenting the information in the

form of a graph or table. The goal is to make sense of

https://orcid.org/0000-0002-8405-0018

large amounts of data and attract attention to the

essential information, enabling informed decision-

making on different handling options.

A row of studies evidences that dashboards

frequently fail to provide information accurately and

efficiently, focusing on the decoration rather than

user and content (Aksu et al., 2019). The ultimate role

of a dashboard can be described as establishing a kind

of “communication bridge” between vast amounts of

digital data and human workers who are able to

process only a limited amount of it. The user and the

ability to make efficient decisions should be at the

center of any dashboard design. As fairly stated in

(Franklin et al., 2017), in socio-technical systems,

there are many difficulties in managing the needs of

individuals, teams, and organizations along with the

applied technology. Therefore, it is critical to put the

end-user in the focus of the dashboard design for

value creation. This study aims to analyze recent

publications on dashboard design and use in the

organizational context of different industrial domains

(i) to identify to which extent the design considers the

end-user and his/her environment so that (ii) the

Revina, A.

Exploring Dashboards as Socio-technical Artifacts: Literature Review-based Insights.

DOI: 10.5220/0010639702070218

In Proceedings of the 18th International Conference on e-Business (ICE-B 2021), pages 207-218

ISBN: 978-989-758-527-2

207

dashboard can provide the necessary support. The

focus lies on the usage of dashboards in the context of

various organizational processes’ support. Hence,

industrial domains, goals, end-users, design process

and dashboard characteristics, technologies used for

dashboard development, and envisioned impacts are

studied in detail.

The remainder of the paper is organized as

follows. The next Section 2 provides the background,

followed by the research methodology in Section 3.

Section 4 and Section 5 present the analysis and

results, which are discussed in Section 6. The

conclusion in Section 7 summarizes the study

findings.

2 BACKGROUND

2.1 Socio-technical Systems and Design

Challenges

Nowadays, people use technology to accomplish

various tasks and be efficient and productive. A socio-

technical approach aims to consider people and

technology from an integrated perspective. Originally,

socio-technical systems (STS) were used in the

organizational redesign. Accordingly, social and

technical factors are equally important in the new

work system design (Mumford, 1994). At present,

STS go far beyond organizational redesign and

include technology, social interactions, environmental

dynamics, and people’s practices (Goggins et al.,

2017). However, STS preserve this fundamental logic

of joint optimization of social and technical elements

in conformity with the organizational goals (Baxter &

Sommerville, 2011).

A considerable volume of Information Systems

(IS) research is based on the STS paradigm and

directly or indirectly draws on its core principles

(Hirschheim & Klein, 2012). STS is believed to have

the potential to bring together diverse IS dimensions

in the future (Sarker, Chatterjee, Xiao, & Elbanna,

2019).

Due to the close interweaving of social and

technical elements, the IS design process, while

focusing on technical artifacts, cannot be separated

from soft, social, cultural, and even psychological

components (Nissen, Bednar, & Welch, 2007).

Accordingly, the IS design can be naturally considered

socio-technical and challenging.

As a rule, these challenges come from different

sources. In the context of STS, according to (Bossen,

2018), one can name two major challenges. One is

caused by the need to choose between various

priorities, desires, and motivations for developing a

new technology solution, i.e., various design

rationales. Another challenge is how one perceives,

identifies, and reflects the environment, including

work activities and procedures, into which new

technology solutions are to be integrated (Bossen,

2018).

In the first challenge, many questions emerge

when considering the variety of organizational

domains, multiple stakeholders, and goals. For

example: Are there domain specificities in the STS

design? What are the goals? Who will be the primary

beneficiary? It becomes evident that often it has to be

decided between one or more competing design

rationales (Goggins et al., 2017). As a rule, IS

designers have difficulty determining the rationale

and need to conduct a stakeholder analysis to identify

who has what stakes in the new technology solution

and their potential to help or impede its progress

(Eskerod, Huemann, & Savage, 2015). Furthermore,

the design goals might be influenced by the funders

(Goggins et al., 2017).

The successful adoption of technology is directly

dependent on its appropriateness and specific

rearrangements in the users’ work. Thus, apart from

the funders and their goals, the end-users have to be

the first to consider in the design process (Bossen,

2018). This point paved the way for the STS studies

being one of the key outcome of Tavistock’s socio-

technical approach in the 1950s (Trist & Bamforth,

1951), followed by the participatory design at the end

of the 1980s (Simonsen & Robertson, 2013)

remaining crucial for modern STS studies (Baxter &

Sommerville, 2011). To sum up, the design,

development, and implementation of new technology

solutions are an intrusion into the existing

environment and, hence, shaped by the stakeholders,

end-users, and their needs and preferences.

Regarding the second challenge, it is important to

understand the current state of the environment, i.e.,

answering the questions: What are current work

procedures? What needs to be changed? Hereby,

another difficulty is a high context dependency and

the possibility of multiple interpretations of how the

work procedures are perceived. Following (Bossen,

2018), first, STS design strongly advocates for

involving the stakeholders and end-users in the design

process. Second, STS design entails deciding

between various design rationales reflecting different

work preferences. Third, STS design demands an

adequate representation of current work processes

(Bossen, 2018).

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2.2 Dashboards and Socio-technical

Systems Design

The study (Sarikaya et al., 2019) reveals that the term

dashboard denotes an extensive range of entities,

defying a typical dashboard definition used in the

visualization community, i.e., “a visual display of

data used to monitor conditions and/or facilitate

understanding”

(Wexler, Shaffer, & Cotgreave,

2017).

At present, dashboards are used almost in every

domain. As a result, the dashboard definition has

developed beyond single-view reporting screens to

incorporate interactive interfaces with many

objectives, such as communication, learning,

motivation, and traditional monitoring and decision

support (Sarikaya et al., 2019).

Dashboards address the problem of large amounts

of data, making it “accessible” for humans and

enabling informed decision-making. STS design

perspective of a dashboard requires at least applying

those design principles and practices aligned with the

way people see and think (Sarcevic, Marsic, & Burd,

2018). Users should be provided with dashboards that

meet their needs and facilitate insight delivery,

improving their decisions. Hence, it is critical to

include the end-users in the design process and

enhance the user experience (Vazquez-Ingelmo,

Garcia-Penalvo, & Theron, 2019).

The customization approach addresses this

demand by assisting developers and users in

configuring customized solutions. For example, the

already mentioned Tableau software enables the

creation of dashboards without any programming

skills. However, in many cases, users have difficulty

determining which configuration fits their goals

(Padilla, 2018). Whereas many solutions exist, recent

studies demonstrate the importance of user goals and

context for designing meaningful, engaging

dashboards (Aksu et al., 2019; Sarikaya et al., 2019).

Focusing on the STS perspective, the following

four categories of dashboard design survey proposed

in (Sarikaya et al., 2019), which partially overlay the

STS design challenges outlined in Section 2.1, are

used: (1) purpose or the intended use of a dashboard

which is supposed to determine the visual and

functional characteristics; (2) audience which is also

known to reflect these characteristics; (3) visual

features and interactivity that are critical for users’

engagement; (4) additional data semantics, such as

alerting about anomalies, breaking of the defined

thresholds, and automatic updates based on the new

data.

As can be concluded, the design principles of STS

and dashboards are congruent in many points and

complement each other with specific design

characteristics. These points, i.e., user-centricity and

goals, current and future work procedures, and design

process and dashboard characteristics, serve as a

guideline to address the first goal stated in the

introduction section, i.e., identify to which extent the

design process considers the user and his/her

environment. To address the second goal of the

present study, i.e., identify if the dashboard can

provide the necessary support, another important point

of the impacts or results of the dashboard application,

which can be addressed in terms of user feedback or

potential propositions of the dashboard designers, is

outlined.

3 RESEARCH METHODOLOGY

3.1 Research Objectives and Questions

In the light of existing advanced software solutions

on the one hand and reported failures in the dashboard

design on the other, this study aims to perform a

systematic review of the most recent literature and

identify to which extent the dashboard design

considers the user and his/her environment and is able

to provide the necessary support. Accordingly, the

following research questions were posed:

RQ1: In which domains has the dashboard research

been recently performed?

RQ2: What are the goals of organizations researching

and applying dashboards?

RQ3: Who are the end-users, and to what extent are

their needs and preferences considered in the

dashboard design?

RQ4: What technologies are used in dashboard

design?

RQ5: What are the (envisioned) impacts of dashboard

research and application in organizations?

3.2 Selection Criteria and Research

Method

The review process is based on the guidelines

indicated in (Kitchenham, 2004). The data collection

process outlined in the PRISMA diagram (Moher et

al., 2009) was used to specify the actions (see Figure

1). In the first identification step, the keyword

“Dashboard” was used to search for relevant

documents in the Scopus database in the title and

abstract of the documents. The search was limited to

English language and journal and conference

proceedings publication type. To review the most

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recent literature on the topic, the publication period

was set to 2017 – 2021. As the study focus is the

design of dashboards in the organizational context

across different domains, the search was limited to the

Engineering, i.e., design, subject area and not to a

particular domain. The selection of particular

domains would result in a thematically narrow set of

studies. In the second screening step, the search

results were refined by the exclusion of the

documents containing keywords that (i) have a strong

focus on and are too specific in terms of the

technology, for example, “Virtual Reality”, “Internet

of Things”, “Smartphones”, “Embedded Systems”,

“Advanced Driver Assistance Systems”, “Computer

Vision”, “Mobile Applications”, “Data Mining”,

“Application Programs”, “Cloud Computing”,

“Network Security”, “Learning Algorithms”,

“Raspberry Pi” and (ii) are irrelevant for the

organizational context, such as “Roads and Streets”,

“Vehicles”, “Cameras”, “Smart City”, “Accidents”,

“Intelligent Vehicle Highway Systems”. This allowed

to balance between width and depth of the study and

obtain the results grasping various cases in the

organizational context and not explicitly focused on

particular technology outside the mentioned context.

Figure 1: Review process phases and outcomes using the

PRISMA flow diagram.

Afterward, the title and abstract of the identified

documents were screened, and the documents were

excluded due to their irrelevance, insufficient quality,

or unavailability of full texts. The relevance was

determined based on the research questions,

considering the following criteria: (1) the articles

should include a case study from a particular domain,

(2) report on the goals of organizations developing

dashboards, (3) report on the end-users, (4) report on

the design process, (5) report on the dashboard

characteristics such as visualization, interactivity,

and data semantics (alerts, automatic updates)

ideally supported by a screenshot, figure, or link to a

dashboard and the description of technologies used

to develop a dashboard, (6) report on the envisioned

impacts of the dashboard research and application.

In the third eligibility step, the check of full texts

was performed, and the documents were excluded

due to the lack of the elements necessary for the

analysis and insufficient description of the obtained

results. Hence, the final sample for the review

included 31 articles covering 31 unique cases

correspondingly (see Figure 1 for details).

4 ANALYSIS

Based on the STS and dashboard related design

challenges specified in the background section, each

article was checked and coded according to the

following aspects: (1) domain and country of

organizations developing dashboards (Are there

domain specificities in the STS design?), (2) goals and

motivations of organizations to develop and apply

dashboards (What are the goals?), (3) if there is an

evaluation performed with end-users, (4) who are the

end-users (Who will be the primary beneficiary?), (5)

how the needs and preferences of end-users are

considered in terms of the design process (What are

current work procedures? What needs to be

changed?), (6) visual, interaction, or data semantics

characteristics of a dashboard, (7) technologies used

to develop a dashboard, (8) envisioned impacts of

dashboard research and application.

The coding procedure included four main steps.

First, a simple annotation framework and coding rules

were created regarding the goals of dashboard design,

dashboard users, design process, dashboard

characteristics, technologies, and impacts. Second,

the framework and rules were discussed following the

collegial advice of another independent researcher.

Third, each paper was carefully studied and coded

using the annotation framework. During this process,

the annotation framework was adjusted three times.

Fourth, the results were double-checked, involving

the advice of the independent researcher. Important to

note that one article could contain several code values

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in the same category, i.e., several goals, end-user

groups, domains, and business areas.

In particular, to answer the RQ1, the specific

application cases were extracted what resulted in 15

industrial domains and two business areas. To answer

the RQ2, the same bottom-up procedure was followed.

For each article, specific goals were identified and

generalized into 26 goals in the form of verbs and

verbal expressions. While extracting the information

regarding end-users, the specific 16 cases were

identified. To address the question to what extent the

end-user needs and preferences are considered in the

dashboard design, the following four points were

considered: (1) evaluation (if conducted or not), (2) as-

is and to-be analysis (if conducted or not), (3)

visualization (multiview, suitable logical, simple

basic, complex) and interactivity (little / no

interactivity, simple basic interactivity in the form of

embedded buttons, drop-down menus, advanced in

the form of filters, resort, drill-down, hovers)

characteristics, (4) data semantics (alerts, automatic

updates, no data semantics). Concerning the RQ4, the

case study implementation section of the articles was

examined and identified 17 different technologies and

two proprietary solutions used for dashboard

development. Finally, to answer the RQ5, the design

and discussion sections of the articles were considered

to analyze the (envisioned) impacts of dashboard

research and application in organizations.

5 RESULTS

This section describes the study findings in relation to

the research questions obtained using the approaches

presented in Section 4

. Section 5.1 introduces the

domains where recent research on dashboards has

been conducted. Section 5.2 summarizes the goals of

dashboard research and application in organizations.

Section 5.3 describes the end-users, whereas Section

5.4 addresses the points related to the design process,

dashboard characteristics, and technologies used for

dashboard development. Finally, Section 5.5 presents

the (envisioned) impacts of dashboard research and

application in organizations, i.e., the envisioned

support.

5.1 Domains of Dashboard Research

Dashboards serve to enhance human cognitive and

perceptual abilities. As a result, dashboards have

Check the complete Excel file with the study results

https://github.com/revina825/Dashboards

become a standard tool to support decision-making in

different domains and business areas, the trend

proven by the abundance of advanced solutions on the

market.

Recent applied research on dashboards evidences

a considerable number of studies aimed at solving

particular practical challenges. The study sample

reveals a high variety of dashboard research domains,

with the majority in the Information Systems (five

cases (Brandt, Striewe, Beck, & Goedicke, 2017;

Cardoso, Vieira Teixeira, & Sousa Pinto, 2018; Fleig,

Augenstein, & Maedche, 2018; López et al., 2021; Pa,

Karim, & Hassan, 2017)), Manufacturing (four

cases (Raudberget, Ström, & Elgh, 2018; Steenkamp,

Hagedorn-Hansen, & Oosthuizen, 2017; Vilarinho,

Lopes, & Sousa, 2017; Yusof, Othman, & Yusof,

2018)), Construction and Real Estate (four cases

(Ho, Mo, Wong, & Leung, 2019; Mahadzir, Omar, &

Nawi, 2018; Montaser & Montaser, 2017; Utrilla,

Górecki, & Maqueira, 2020)), Healthcare (three

cases (Christen et al., 2020; Franklin et al., 2017;

McGlothlin, Srinivasan, & Stojic, 2019)), and Supply

Chain (three cases (Ho et al., 2019; Lanotte, Ferreira,

& Brisset, 2020; Martins, Alves, & Leão, 2018)).

However, Sales (Noonpakdee, Khunkornsiri,

Phothichai, & Danaisawat, 2018; Telaga, Librianti, &

Umairoh, 2019) and HR (Chattopadhyay et al., 2020;

Zajec, Mrsic, & Kopal, 2021) company areas, i.e.,

primary sources of company performance indicators,

are underrepresented along with Automotive and

Airspace domains with only two cases. Machinery

(Longo et al., 2018), Banking (Massardi, Suharjito, &

Utama, 2018), Academia (Wibowo, Andreswari, &

Hasibuan, 2018), Disaster Management (Saha,

Shekhar, Sadhukhan, & Das, 2018), Government

(Conejero et al., 2021), Telecom (Fraihat, Almomani,

Fraihat, & Awad, 2020), and Tourism (Balletto,

Milesi, Ladu, & Borruso, 2020) are unique cases in

the sample. See Figure 2 for the relative distribution

numbers.

The identified domains and cases differ in the

research artifact, at which a dashboard is targeted,

end-user group, and application area. For example,

the cases in IS domain aim at supporting IS managers,

designers, or software developers in monitoring the

health status of the IS (Cardoso et al., 2018),

measuring green IS design (Pa et al., 2017),

discovering important processes in IS (Fleig, 2017),

supporting software engineering and development

(Brandt et al., 2017; López et al., 2021).

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211

Figure 2: Domain distribution in dashboard research.

The absolute distribution of countries is presented

in Figure 3. The majority of cases were conducted in

the EU (ten cases). However, considering each EU

country separately, Malaysia and Indonesia make up

the majority, with three cases per country.

Figure 3: Country distribution in dashboard research.

5.2 Goals of Dashboard Research and

Application

Similar to the domains, the analysis shows a broad

range of goals in the dashboard research and

application. To provide a structured outcome, the

identified 26 unique goals were grouped into four

categories based on their function (see Table 1). In the

Information & Knowledge Management category,

along with the typical dashboard goals such as

visualize, summarize, inform, two studies highlight

specific motivations for dashboard design, i.e.,

simplify and standardize the information collection

(Raudberget et al., 2018) and storing and retrieving

(Cepeda & Lopes, 2019).

Table 1: Four categories of goals.

Category Unique goals

Information &

Knowledge

Management

(31% of cases)

visualize, summarize, inform, simplify

and standardize, store and retrieve

Project

Management

(20% of cases)

monitor, alert, organize and coordinate,

measure, report, communicate, improve

communication, support in project

planning, manage

Decision-making

(33% of cases)

support decision-making, predict,

identify, provide data insights, discover,

simulate, explore

Secondary goals

(16% of cases)

improve, promote, valorize, evaluate,

analyze, reduce

In the Project Management category, both typical

characteristic goals (monitor, measure, report,

communicate) and more comprehensive goals

organize, coordinate (Christen et al., 2020), and

manage (Lanotte et al., 2020) are named.

Noteworthy, the majority of studies on dashboards set

the goals related to the decision-making support.

Behind such advanced motivations as discover (Fleig

et al., 2018) or simulate and explore (López et al.,

2021) are complex technology solutions. Finally, in

the row of studies, secondary, often even more

important goals are mentioned, for example, reducing

negative influences (Pa et al., 2017) and valorizing

certain objects (Balletto et al., 2020).

5.3 End-users of Dashboards

In total, 16 unique end-users are identified in the

sample. Afterward, the end-users were grouped into

five meaningful categories (see Table 2). The

category “Others” contains domain- and case study-

specific users such as process owners, product

developers, software developers, tourists, and real

estate players. It is observed that the end-users are

defined and considered in accordance with the

domain and case study specificity and goals.

Table 2: End-users of dashboards.

End-users Source

Managers

(51% of cases)

(Brandt et al., 2017; Cepeda & Lopes,

2019; Chattopadhyay et al., 2020; Fleig et

al., 2018; Fraihat et al., 2020; Ho et al.,

2019; Kapp, Lefebvre, & Monnier, 2019;

Lanotte et al., 2020; López et al., 2021;

Martins et al., 2018; Massardi et al., 2018;

McGlothlin et al., 2019; Montaser &

Montaser, 2017; Noonpakdee et al., 2018;

Steenkamp et al., 2017; Telaga et al., 2019;

Utrilla et al., 2020; Vilarinho et al., 2017;

Yusof et al., 2018; Zajec et al., 2021)

Employees (10% of

cases)

(Cepeda & Lopes, 2019; Ho et al., 2019;

Vilarinho et al., 2017; Yusof et al., 2018)

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Table 2: End-users of dashboards (cont.).

Public institutions,

healthcare,

government

(18% of cases)

(Balletto et al., 2020; Christen et al., 2020;

Conejero et al., 2021; Franklin et al., 2017;

McGlothlin et al., 2019; Saha et al., 2018;

Wibowo et al., 2018)

IS administrators,

designers, technical

staff (8% of cases)

(Cardoso et al., 2018; Longo et al., 2018;

Pa et al., 2017)

Others (13% of

cases)

(Balletto et al., 2020; Fleig et al., 2018;

López et al., 2021; Mahadzir et al., 2018;

Raudberget et al., 2018)

5.4 Dashboard Design, Characteristics,

and Technologies

In the dashboard design context, the question

regarding the as-is and to-be analysis, i.e., current

work procedures and what needs to be improved, was

addressed in the study. In most cases (65%), both

analyses were performed what is in line with the STS

design approach. In the dashboard characteristics,

visualization, interactivity, and data semantics were

considered. According to the developed coding

scheme, visualization reveals the following

distribution: multiview 40%, suitable logical 28%,

simple basic 26%, complex 7%. The study results

show 29% of dashboards with simple basic

interactivity, 26% advanced, and 45% of dashboards

with little or no interactivity. Regarding data

semantics, only 16% of cases evidence alert function

and 25% - automatic updates.

In order to develop dashboards with mentioned

characteristics, a diverse set of technologies was used.

19 technologies were grouped into six categories. As

follows from Table 3, Microsoft solutions are in a

clear majority.

Table 3: Six categories of technologies.

Category Technologies

Programming languages

(22% of cases)

Java, PHP, Python, R

Microsoft (34% of cases)

MS Access, MS Share Point,

MS Excel, VBA, MS Power BI

Other well-known

solutions (19% of cases)

Qlick, Tableau, Oracle

Less known solutions

(13% of cases)

Pureshare, Freeboard, Axure RP

9, Pajek

Google (6% of cases) Google sites, Google sheets

Proprietary (6% of cases) n.a.

5.5 Impacts of Dashboard Research

and Application

In this subsection, the envisioned impacts of

dashboard research and application in organizations

are presented. First, this information was extracted

from each of the articles in the sample. Afterward, 16

categories were developed.

The majority of cases (25%) name performance

improvement as one of the impacts of dashboard

application, for example (Ho et al., 2019; Massardi et

al., 2018; Telaga et al., 2019). It is followed by

closely related efficiency improvement (15%), for

example (Cardoso et al., 2018; Franklin et al., 2017;

Longo et al., 2018), and better data monitoring

(13%), like in (Steenkamp et al., 2017). Time

improvements (10%) (Yusof et al., 2018), better

(service) quality (9%) (Wibowo et al., 2018), and

better organization and coordination (6%) (Lanotte

et al., 2020) are less frequently encountered impacts.

Better planning (Saha et al., 2018), sustainability

contribution (Utrilla et al., 2020), better

understanding (Chattopadhyay et al., 2020), better

awareness (Fleig et al., 2018), and cost reduction

(McGlothlin et al., 2019) have been mentioned only

in 3% of cases. Finally, only 1% of cases reveal better

valorization (Balletto et al., 2020), better policies and

strategies, employment improvement (Conejero et al.,

2021), better documentation (Raudberget et al.,

2018), and better communication (Montaser &

Montaser, 2017).

6 DISCUSSION

6.1 Dashboard Research and

Application Considerations

Although dashboards take their origin in automobiles

and other vehicles, they have become increasingly

popular in business, government, and nonprofit

organizations. At present, dashboards are widely

known to provide business executives and managers

with company performance-related information, for

example, sales, HR, or profit (Eckerson, 2010). On

the one hand, it is confirmed by the study results

revealing managers (51%) as a major end-user group.

On the other, sales (6%) and HR (6%) business areas

are underrepresented in the sample.

Noteworthy, with the high domain variety, i.e.,

Automotive, Healthcare, Construction, Government,

Academia, Banking, to name a few, the study results

demonstrate a high interest in dashboards in the IS

discipline itself. The IS researchers set specific goals,

such as monitoring IS health status (Cardoso et al.,

2018), reducing the environmental impact of ICT

products and services (Pa et al., 2017), discovering

important business processes (Fleig et al., 2018),

extracting and visualizing high-level strategic

Exploring Dashboards as Socio-technical Artifacts: Literature Review-based Insights

213

indicators related to software quality (López et al.,

2021), and supporting Software Engineering projects

(Brandt et al., 2017). In their majority, these studies

make use of programming languages such as Java to

develop dashboard solutions.

The larger part of the studies has been performed

in the EU countries whereby (i) consistent approaches

of user involvement in terms of evaluation, as-is and

to-be analyses have been followed, and (ii) existing

software, such as Microsoft, Qlick, Axure, was used.

In general, the analysis reveals a strong trend of

leveraging vendor solutions rather than building the

dashboards from scratch. In contrast, one decade ago,

45% of companies that participated in the survey

declared developing proprietary solutions (Eckerson,

2010). This observation evidences the high

customization and usability of commercial software.

As also noted in (Eckerson, 2010), at present,

dashboards can be considered Business Information

Systems comprising data collection, integration, and

processing technologies. In the sample, the studies

focus on both (i) dashboards as mere visual

interfaces (Christen et al., 2020; Franklin et al., 2017;

Wibowo et al., 2018) and (ii) dashboards as part of

comprehensive solutions. Thus, (Conejero et al.,

2021) propose a multi-aspect support system using

Data Engineering and advanced visualization

techniques as well as association rules. (Fleig et al.,

2018) develop a decision support system for

identifying the most critical business processes in IS

comprising the IS layer, data management layer, and

visualization layer. Introducing an analytic dashboard

visualization for flood management, (Saha et al.,

2018) describe an architecture of the decision support

system. At the same time, (Wibowo et al., 2018),

while evaluating the proposed dashboard, highlight

its suitable and logical visualization but declare the

need for decision support. Hence, a dashboard should

not be considered as an isolated visual interface but

rather a part of a comprehensive decision-making

support solution to facilitate value creation and

proactively assist the end-user.

In the context of the design process, STS research

highlights the importance of performing both as-is

and to-be analyses (Bossen, 2018; Goggins et al.,

2017). In the sample, most of the studies (65%)

follow this approach. Moreover, (Franklin et al.,

2017) refer to STS while analyzing the challenges in

the implementation and lessons learned. (Martins et

al., 2018) use participatory design, which is a key

issue in the STS design (Scacchi, 2004), to develop

and implement operational monitoring dashboards in

a lean context.

Evaluation, an essential step in any design

process, has been mentioned in 52% of cases. In 32%

of papers, case studies were used to develop a

dashboard, however, without any evaluation. In 16%,

the whole approach (and not a dashboard in

particular) was evaluated. In a few studies, the

researchers worked with open-source data (Balletto et

al., 2020; Fraihat et al., 2020). In some cases,

evaluation is planned as a part of future work

(Christen et al., 2020). It is to note that for successful

design and implementation of dashboards as socio-

technical artifacts, the evaluation with end-users

plays a key role. The evaluation results should be

reported straightforwardly and comprehensively,

which was not the case in any of the articles in the

sample.

6.2 End-user Support Implied in

Dashboards

As stated in the previous subsection, there is

insufficient end-user involvement in the design

process of dashboards and related solutions.

Nonetheless, the end-user support is implicitly

contained in the dashboard goals, characteristics, and

envisioned impacts.

The analysis of the goals of researching and

applying dashboards in organizations shows that the

goals are prevailingly concerned with decision-

making support (33%), i.e., predicting, identifying,

discovering, simulating, exploring, and providing

data insights. This observation is in line with the

declared end-user demands (Wibowo et al., 2018).

Along with the decision-making support, such typical

dashboard goals as Information & Knowledge

Management (31%) and Project Management (20%),

and several secondary goals (16%) are mentioned.

Whereas all these goals are directed towards assisting

and empowering the end-user, he/she is not explicitly

discussed in the sense of usability and satisfaction

while introducing these goals. It should be

emphasized that in the STS context, the end-user

needs to be explicitly addressed while setting the

design goals.

The underrepresented end-user perspective in the

goal-setting is also reflected in the dashboard

impacts. Hereby, a clear focus is set on performance

(25%) and efficiency (15%), followed by data

monitoring (13%), time improvement (10%), and

better (service) quality (9%). Hence, end-user

satisfaction and other benefits related to the workload

reduction are STS-related critical missing points in

the literature, demanding thorough consideration.

E-DaM 2021 - Special Session on Empowering the digital me through trustworthy and user-centric information systems

214

Many dashboard projects fail as they mainly aim

at making a “glitzy” interface (Aksu et al., 2019;

Eckerson, 2010). The study results evidence the

prevalence of multiview (40%), suitable logical

(28%), and simple basic (26%) visualizations. Few

studies (7%) introduce dashboards with complex

information presentation (Conejero et al., 2021; Zajec

et al., 2021). In contrast, the interactivity and data

semantics characteristics need to be improved due to

the prevailingly no / little interactivity (45%) and

missing data semantics (50%). While high

interactivity and data semantics can potentially

increase end-user satisfaction, it is to argue that

dashboard characteristics, including data, data

semantics, visualization, and interactivity types,

should be selected in line with end-user needs, tasks,

and preferences. Such an aligned dashboard design

would lead to end-user satisfaction and acceptance,

as also noted in (Bossen, 2018). It follows that closely

studying the interaction between the end-user and the

dashboard is an essential factor requiring more focus

from the research community and practitioners.

It is to highlight that, similarly to (Isazad

Mashinchi, Ojo, & Sullivan, 2020), the analysis did

not clearly state if there is a relationship between

dashboard characteristics mentioned above (also

described in Section 5.4) and envisioned impacts of

dashboard application (Section 5.5). I.e., the question

remains if one can improve or influence the impacts

by modifying the dashboard characteristics. It is

essential to respond to this question since it

demonstrates the role of various characteristics in

dashboard design.

6.3 Limitations

A literature review can be considered an excellent

methodological instrument for addressing a wide

range of research issues (Snyder, 2019). Nonetheless,

it has several limitations. In this regard, the study

evidences the aspects listed below.

The term dashboard is constantly penetrating

different areas and taking on new meanings.

Generally denoting visual displays used for showing

important information at a glance (Few, 2017),

dashboards are applied and researched in diverse

domains ranging from the cities and buildings, cars

and highways to organizations aiming to support

managers in making informed decisions or

monitoring various organizational processes. The

latter is the focus of the study and, due to its

seemingly broad coverage, makes up the significant

limitation of the study.

To address this limitation and filter out the

unrelated works, the exclusion of irrelevant keywords

was used. However, keywords are limited and cannot

embrace all the aspects of the work. Moreover,

keywords are usually adjusted to fit the scope of the

target journal or conference. Hence, while filtering

the keywords, relevant studies could be missed.

Authors’ bias is another common limitation of

(systematic) literature reviews (Denyer & Tranfield,

2006). Although the transparent procedure attempts

to reduce the subjective effect, the authors are never

entirely neutral while reviewing the literature (Kraus,

Breier, & Dasí-Rodríguez, 2020).

A further challenge is addressing various study

contexts, especially in the broad and highly

fragmented research fields, like organizational

studies and management (Denyer & Tranfield, 2006).

Even in comparable studies, in complex social

contexts, there are always likely to be minor

differences. Synthesizing the studies to achieve a

structured outcome can remove critical contextual

information (Hammersley, 2001).

7 CONCLUSION

In this study, the analysis of dashboards’ research and

application in organizations was performed with a

socio-technical emphasis. Based on a systematic

literature review, a sample of 31 articles was selected.

Various aspects such as domains, goals, design

process and dashboard characteristics, technologies,

and impacts of dashboards’ application in

organizations were considered. It was identified that

in the majority of cases, users are involved in the

evaluation process. This way, helpful improvement

suggestions can be gathered. For example, the users

place a special value on the decision-making support

functionalities and not visual characteristics.

Further, several gaps have been identified: (i) the

evaluation process was lacking thorough and

comprehensive documentation and reporting; (ii) the

user-centricity, for example, end-user satisfaction,

workload and stress reduction, is insufficiently

expressed in the dashboards’ goals and envisioned

impacts (the focus lies on business performance and

efficiency increase); (iii) dashboard characteristics

should be selected in line with end-user needs, tasks,

and preferences; (iv) missing evidence on the relation

between dashboard characteristics and envisioned

impacts of the dashboard application. Future studies

addressing these gaps and putting more emphasis on

(i) dashboards as part of comprehensive decision-

making support solutions and (ii) end-user

Exploring Dashboards as Socio-technical Artifacts: Literature Review-based Insights

215

involvement in all stages of the dashboard design and

implementation have a high potential to improve

value creation and user satisfaction in this field.

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