Business Impacts of Data Analytics in the Service Sector:

A Systematic Literature Review

Maria Madlberger

and Mykhailo Yesaulov

Department of Business and Management, Webster Vienna Private University, Praterstrasse 23, 1020 Vienna, Austria

Keywords: Data Analytics, Service Sector, Big Data, Artificial Intelligence, Systematic Literature Review.

Abstract: The service sector is one of the industries that is most affected by digital technology and data analytics.

Despite a large body of literature on the effects of various data analytics techniques, a comprehensive review

of academic insights into impacts of data analytics in the service sector is missing. The goal of this paper is a

systematic literature review of impacts that data analytics techniques exert on the performance of service

provision. A sample of 70 scholarly articles has been identified and analyzed. A majority of the analyzed

articles addresses data analytics in general, big data analytics techniques, or artificial intelligence, whereas a

lower number of studies investigates the impacts of concrete data analytics techniques. The impacts of data

analytics can be categorized into factors that relate to customer responses, management and decision-making,

and long-term indirect effects on competitiveness and monetary impacts. The findings further show that data

analytics based on big data analytics techniques yields different outcomes than analytics approaches that are

based on artificial intelligence.

1 INTRODUCTION

Data analytics has gained significant importance with

an increasing availability of large volume data and

data analysis capabilities. This development has

facilitated the emergence of new business processes

(e.g., industry 4.0), the creation of new business

models, such as value co-creation models, and the

transformation of entire industries, referred to as

digital business transformation (Akter et al., 2022).

Digital business strategies and organizational

capabilities are key drivers of digital transformation

(Nadeem et al., 2018).

Digitalization and data analytics have the

potential to transform any industry and one of the

most affected ones is the service sector. This industry

has experienced foundational transformations, which

occur on multiple levels. Services contain numerous

interactions with customers that can result in

customer data. This allows real-time responses to

customer demand, behavior, and events in service

provision. Data analytics provides insights into

customer behavior that go beyond traditional insights

from transaction data and empirical data collection

(Kumar et al., 2013). Many components of service

https://orcid.org/0000-0003-2850-0499

provision can be digitized so that besides an enhanced

data-driven exchange of information between service

provider and customer, data analytics enables the

application of innovative technologies with enhanced

interaction with the customer, such as robotics and

chatbots (Sun & Wang, 2022). Illustrative examples

are fintechs, a business model that has transformed

the financial services sector and led to a new category

of service providers with altered ways of delivering

services (Werth et al., 2023), or peer-to-peer

platforms such as Airbnb in the hospitality sector (K.

H. Lee & Kim, 2019).

Evidence of the transformative potentials of data

analytics in different economic sectors is manifold

and growing fast. The diversity of applications has

resulted in a multitudinous array of impacts in various

ways which makes it difficult to obtain a

comprehensive overview of the state of the art of

research on impacts of data analytics on business.

Scholars have addressed this need by providing

literature reviews in an array of industries, such as

manufacturing (Getachew et al., 2024), supply chain

management (Darbanian et al., 2024), healthcare

engineering (Salazar-Reyna et al., 2022), or

innovation research (Natividade Joergensen & Zaggl,

Madlberger, M. and Yesaulov, M.

Business Impacts of Data Analytics in the Service Sector: A Systematic Literature Review.

DOI: 10.5220/0013210900003929

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 27th International Conference on Enterprise Information Systems (ICEIS 2025) - Volume 1, pages 27-38

ISBN: 978-989-758-749-8; ISSN: 2184-4992

2024). However, to date no systematic literature

review within the service sector could be identified.

Against this background, the objective of this study is

to provide a first systematic review of scholarly

research on concrete effects of data analytics

techniques in the service sector. We seek in particular

to address the following questions:

What impacts of various advanced data analytics

techniques in the service sector are identified in

scholarly literature?

What are variations of effects by data analytics

techniques and industries within the service sector?

The insights obtained from this systematic review

allow a comprehensive overview of the state of the art

of scholarly research on an industry that is

particularly affected by data analytics in various

ways. It helps scholars to develop a research agenda

for an enhanced investigation of the potentials of data

analytics techniques in different industries within the

service sector and thus to get a better understanding

of impacts of data analytics on customer experience

and behavior, operational management, and long-

term strategic benefits for service providers.

2 DATA ANALYTICS

TECHNIQUES

Data analytics is the capability of obtaining insights

from data by carrying out different, primarily

quantitative analysis methods, such as statistics and

mathematics, econometrics, optimizations,

simulations, classifications, or other methodologies to

improve decision-making (G. Wang et al., 2016). It

thus describes a set of different analysis techniques and

methodologies. From a data point of view,

contemporary data analytics is usually based on big

data, whereby analysis methods and algorithms

primarily utilize datasets that exceed the capacity of

traditional databases. Big data analytics is

characterized by five features that distinguish big data

from conventional databases: Volume refers to the

large amount of data, variety denotes a high number of

different types and formats of data, velocity stresses the

high speed at which data is retrieved as well as

analyzed (often in real-time), and veracity describes

the correctness and thus trustworthiness of analyzed

data (Akter et al., 2022). Value refers to the potentials

of big data analytics to obtain insights that can unlock

benefits to an organization such as customer centricity

or innovative product or service offerings.

From an analytics process viewpoint,

contemporary data analytics increasingly relies on

artificial intelligence (AI) and is intertwined with big

data analytics (Duan et al., 2019). While big data

analytics does not necessarily require AI tools for

analysis, AI is typically reliant on large amounts of

data (Akter et al., 2022). AI combines different

computing technologies to facilitate rational decision-

making in complex situations and contexts (Duan et al.,

2019). AI capabilities require a system to be able to

conduct natural language processing, retrieve and

process data from large databases, apply mathematical

theories, carry out automatic programming, and solve

critical problems (Nilsson, 2014). An important

prerequisite is the availability of massive amounts of

data that permit an AI system to learn from the data in

one of two ways, i.e., machine learning or deep

learning (Akter et al., 2022).

Machine learning consists of several types:

Supervised learning is based on providing labeled

data that is used to train the system to predict

outcomes resulting from these data. Unsupervised

learning is based on unlabeled and unstructured data

where no target variable is defined (Syam & Sharma,

2018). Another machine learning type is

reinforcement learning. It is based on trial and error,

whereby a positive response to an outcome reinforces

their connection (Rolf et al., 2023). Reinforcement

learning has shown to provide superior outcomes

particular in collaborative conditions where

computing devices are linked (Udayakumar &

Ramamoorthy, 2023). Deep learning processes data

in its raw form, which permits advanced analytics

such as speech recognition, image recognition, object

detection, and others based on unsupervised learning.

Artificial neural networks are an application of deep

learning where analytics takes place on multiple

layers. On each layer, the input is transformed in a

non-linear way and represented as an output whereby

layers are connected via nodes (Lemley et al., 2017).

3 METHODOLOGY

To understand the role of data analytics in the service

sector, a systematic literature review has been

conducted. To capture a significant body of literature,

we conducted a search within the electronic databases

EBSCO Host and Scopus (Diaz Martinez et al., 2024)

and further expanded the search through forward and

backward cross-referencing. The search string was

defined on the basis of literature on data analytics and

includes the following terms: ("data analytics" OR

“machine learning” OR “artificial intelligence” OR

AI OR “deep learning” OR “neural network”) AND

("service sector” OR “service industry”). To ensure

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

that the search results are limited only to high-quality

peer-reviewed journal articles, we only included

publications in journals, which are ranked in the first

or second quartile of the Scimago ranking. Only

articles published 2014 or later in English language

were included in the search.

This literature search, conducted in April 2024,

identified 259 articles after the elimination of

duplicates. The titles, abstracts, and full papers have

then been reviewed to determine their relevance for

the study in terms of the inclusion criteria below. To

be included in the final literature selection,

 articles have to contain an empirical analysis.

This is not limited to specific methods. Hence,

besides the use of various data analytics and AI

for data analysis itself, also papers that apply

other analysis methods of empirical data about

data analytics (e.g., surveys, semi-structured

interviews) are included in the sample. On the

other hand, literature reviews or conceptual

studies were not included in the sample.

 articles have to refer to data analytics, either as

a specific data analytics method or data

analytics in general as an independent variable.

 the research context of articles has to be situated

in the service sector or at least be associated with

service provision (e.g., services in the context of

manufacturing).

This rigorous process led to the final inclusion of

70 articles including cross-referenced articles that met

the above-mentioned inclusion criteria, but were not

found in the initial keyword search. The articles

selected for the analysis were structured in a concept

matrix (Webster & Watson, 2002) on the basis of the

Theories, Contexts, and Methodology (TCM)

framework (Paul et al., 2017). First, we extracted

descriptive information from each article based on

each criterion. We recorded all mentioned theories,

collected information of the concrete service sector

(e.g., finance, hospitality) and geographical scope,

where applicable, and listed all mentioned

methodologies. Next, we inductively coded the

concrete attributes and types of data analytics methods

and the respective dependent variables, i.e., effects of

data analytics discussed in the articles. The

specification of data analytics methods is heterogenous

in the investigated articles. Most articles refer to big

data analytics, artificial intelligence, and data analytics

in general. Coding of the effects of data analytics

resulted in 43 single variables that were further

grouped into 14 categories which belong to two main

areas (impacts on interactions with customers and

internal effects on the organization).

4 RESULTS

4.1 Descriptive Analysis

Out of 70 identified articles, 21 primarily relate to big

data analytics in general, 18 to AI in general, 7 to

machine learning, 16 to further single methods such

as regression-based models, neural networks, or k-

nearest neighbors, and 8 to data analytics in general,

without specifying concrete applications or methods.

The service industry context is discussed as

follows: 16 articles refer to the finance sector (banks,

fintechs, insurances, other financial service

providers). 9 articles refer to the hospitality sector

(hotels and other touristic services), 4 articles deal

with healthcare, another 4 with supply chain

management (SCM), 3 with manufacturing, and 10

with other areas (public sector IT services, smart city

infrastructure, real estate, foodservice, consulting,

direct marketing services, IT Management,

telecommunications, aviation, and small-and

medium-sized enterprises). The majority of articles

(24) addresses the service sector in general, without

referring to a specific industry.

4.2 Categories of Data Analytics

Impacts

The open coding of impacts of data analytics yields

14 groups of effects of data analytics in the service

sector. These groups are the following:

 Decision-making (N=19): Positive effects on

decision-making in the organization, i.e., better

decision outcome or decision-making process.

 Service offerings (N=26): Positive effects on the

service provision, such as new service offerings,

improvement of service quality, or enhanced

customer service.

 Management/governance (N=26): Positive

impacts on specific areas in management and/or

governance, such as risk management, intra-

organizational processes, or planning. It also

includes higher managerial skills and

competencies, such as data management,

compliance, or resource allocation.

 Customer management (N=15): Positive

impacts including improved ways of handling

customer interactions, the process of providing

service offerings to customers, and/or obtaining

customer insights.

 Personalization of services (N=12): More

services tailored to individual customers.

Business Impacts of Data Analytics in the Service Sector: A Systematic Literature Review

 Customer response (N=28): Impacts of data

analytics on customer response, such as

improved relationships of customers with the

company, improved customer experience, and

customer satisfaction.

 Innovation (N=5): Positive impacts on the

organization’s innovativeness, e.g., novel

offerings, innovation management process.

 Monetary impacts (N=13): Positive impacts on

the financial well-being of the organization, i.e.,

profitability, sales, and reduction of costs. These

cost reductions are mainly driven by

improvements of primary business activities,

such as efficiency gains in operations (see

below) and enhanced customer interactions.

 Efficiency (N=26): Positive impacts on service

operations efficiency, particularly by general

efficiency gains in service provision and

efficiency increase in single business domains

such as audits or medical treatments.

 Competitive advantage (N=10): Positive

impacts on the organization’s competitiveness

by yielding competitive advantages.

 Single business administration functions (N=

11): Human resource management (8 articles),

supply chain management (2), marketing (1).

 Corporate social responsibility (CSR; N=7):

Positive or negative social and/or ethical

implications of data analytics for issues such as

privacy, individual and social welfare, or

impacts on the environment.

 Data value (N=4): Positive impacts on the

organization’s ability to enhance the value of

existing data, such as being able to identify fake

reviews or improve predicting capabilities.

 Transparency (N=3): Positive impacts on the

availability and/or accessibility of data and

processes that are supported by data analytics.

4.3 In-Depth Analysis of Data

Analytics Impacts

In the following, the analysis results are presented in

detail. Tables 1 to 5 show the distribution of identified

impacts of big data analytics, AI, machine learning,

specific data analytics techniques, and data analytics

in general on the identified 14 factors in each article.

For the sake of readability, the single business

functions and the categories of CSR, data value, and

transparency are summarized in one column,

respectively.

Overall, the most frequently reported impact of

data analytics across all types is customer response

Table 1: Impacts of big data analytics in the service sector.

Context

Decision-

making

Service

offerings

Management/

governance

Customer

management

Service

pe-sonalization

Customer

response

Innovation

Monetary

impacts

Efficiency

Competitive

advantage

Business

functions/

further areas

Reference

Finance

X X X

(Hajiheydari et al., 2021)

CSR (Arthur & Owen, 2019)

X X

XXX

(

Bratasanu, 2017

)

(

Gozman et al., 2018

)

(

Vo et al., 2021

)

X X

X X X (Herrmann & Masawi, 2022)

SCM

X X SCM (Khan, 2019)

SCM (Gocer & Sener, 2022)

Manufacturing

X X

(

Lu & Xu, 2019

)

X X

X X X X HR

(

Shukla et al., 2019

)

X X

X (Niebel et al., 2019)

Public secto

X X

(Choi et al., 2018)

Service sector

in general

X X X (Cristescu et al., 2023)

X X X X

(

Shirazi & Mohammadi, 2019

)

X X

XHR

(

Parmar & Vid

asa

ar, 2023

)

(

Boldosova, 2020

)

X X (Bumblauskas et al., 2017)

X X

X X (Akter et al., 2019)

X X (Noble & Mende, 2023)

X X X X X

(

Bezuidenhout et al., 2023

)

CSR

(

Belanche et al., 2024

)

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

Table 2: Impacts of artificial intelligence in the service sector.

Context

Decision-

making

Service

offerings

Management/

governance

Customer

management

Service

personalization

Customer

response

Innovation

Monetary

impacts

Efficiency

Competitive

advantage

Business

functions/

further areas

Reference

Finance

X X X (Mogaji & Nguyen, 2021)

X X X (Mogaji & Nguyen, 2022)

CSR

(Lui & Lamb, 2018)

Hospitality

X X (Neuhofer et al., 2021)

X X (Nam et al., 2021)

X X (Li et al., 2022)

X X X (Buhalis et al., 2019)

X X

HR, CSR

(Kandampully et al.,

2022

)

SCM X X X X (Khalifa et al., 2021)

Healthcare

X X X (Sood et al., 2022)

HR, CSR

(Cavanagh et al., 2022)

Smart cit

CSR

(Muhammad et al., 2019)

Service sector

in general

(Gu et al., 2022)

X X X (Ameen et al., 2023)

X X X (Flavian & Casalo, 2021)

X XXX

(Rana et al., 2022)

CSR

(T. Kim et al., 2022)

X X X X X (Wirtz et al., 2018)

(39% of the analyzed articles), followed by service

offerings, management, and efficiency gains (36% of

articles each). The least frequently reported impacts

of data analytics are innovation (7% of articles),

competitive advantages (14%), personalization of

services (17%), and monetary effects (18%).

Improved decision-making (26% of articles) and

customer management (21%) range in the middle.

The analysis of benefits reported by type of data

analytics tools reveals significant differences. In the

context of big data analytics (Table 1), the most

frequently reported impacts are the improvement of

service offerings, decision-making, efficiency gains,

and customer response. Most insights have been

obtained in studies that refer to the service industry in

general. In terms of industry sectors, the finance

sector particularly benefits from improvements that

can be experienced by customers (service offerings,

customer response), whereas improvements in

decision-making and efficiency gains have been

reported less frequently there. When it comes to

impacts of AI (Table 2), positive effects are

particularly reported on customer response,

efficiency, management, and service offerings. The

most frequently investigated single service sector in

this category is the hospitality sector, where reported

effects are rather evenly distributed and show a

similar pattern as studies that do not specifically

relate to a particular service sector.

Studies that focus on machine learning are smaller

in numbers (Table 3), yet report noteworthy effects

particularly on efficiency, compared to evenly

distributed impacts on decision-making,

management, customer response, and monetary

benefits. In this context, studies that were done in the

hospitality sector identify a lower number of positive

impacts than in other or general service sectors.

Articles that investigate specific data analytics

tools (Table 4) primarily report positive impacts on

management, whereas other types of impacts are

identified less frequently than in other data analytics

contexts. In addition, articles in the finance sector

report more positive impacts on customer

management and management/governance, whereas

positive impacts on efficiency and innovation are

found in other industries.

Finally, the studies that do not differentiate

between concrete types of data analytics (Table 5)

predominantly report impacts on the service offering

and management, yet less on decision-making. In this

context, one study done in the foodservice industry

reports most positive impacts, whereas the other or

general service sectors are shown to display evenly

distributed positive effects across the categories.

Business Impacts of Data Analytics in the Service Sector: A Systematic Literature Review

Table 3: Impacts of machine learning in the service sector.

Context

Decision-

making

Service

offerings

Management/

governance

Customer

management

Service

personalization

Customer

response

Innovation

Monetary

impacts

Efficiency

Competitive

advantage

Business

functions/

Further areas

Reference

Hospitality

Data (M. Lee et al., 2022)

(Kalnaovakul & Promsivapallop,

2023)

SCM X Transparency (Tian et al., 2022)

Real estate X X X X (McGrath et al., 2019/20)

Service

sector in

general

X X X X

(Udayakumar & Ramamoorthy,

2023)

X X X X (C.-H. Wang & Lien, 2019)

X Transparency (Titl et al., 2024)

Table 4: Impacts of specific data analytics methods in the service sector.

Context

Data analytics

method

Decision-

making

Service

offerings

Management/g

overnance

Customer

management

Service per-

sonalization

Customer

response

Innovation

Monetary

impacts

Efficiency

Competitive

advantage

Business

functions/

Further areas

Reference

Finance

Vector

representation

X X Marketing (Oh et al., 2023)

Lasso log

regression, decision

tree, neural networ

X Data (Ahn, 2023)

Neural network,

-nearest neighbors

X X

(Holopainen & Sarlin,

2017)

-nearest neighbors X (Rjoub et al., 2023)

Neural network,

classification

X Data

(Ogwueleka et al.,

2012)

Neural network X X (Sharma et al., 2015)

Hospitality

Text analytics

X X

(Mariani & Borghi,

2023)

Neural network X (Peng & Lai, 2014)

Marketing

Hayes process

model

X X X

(Abbu &

Gopalakrishna, 2021)

Software-mediated

Process Assessmen

X X X Transparency (Shrestha et al., 2016)

Telecom

Neuro-fuzzy

techniques

(Abbasimehr et al.,

2012)

Transpor

Neural network X (Zhao et al., 2019)

Service

sector in

general

Data visualization X X X (Tang et al., 2017)

IoT-

ased analytics X X X X X (Karttunen et al., 2023)

Association Rule

Mining, Data

Envelopment

Analysis

X X X Data (C. Kim, 2017)

Data Envelopment

Analysis

X (Zhou & Zhan, 2021)

5 DISCUSSION

This study provides first insights into the role of data

analytics in the service sector. While this scope is

rather broad, it allows to capture a wide range of

publications and resulting insights into

methodological approaches and specific service

sectors that are addressed in research. The identified

categories of data analytics impacts in the service

sector show two mainly affected areas in business.

The first one is impacts on interactions with

customers. These factors are either direct perceptions

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

Table 5: Impacts of general data analytics approaches in the service sector.

Context

Decision-

making

Service

offerings

Management/

governance

Customer

management

Service

personalization

Customer

response

Innovation

Monetary

impacts

Efficiency

Competitive

advantage

Business

functions/

further areas

Reference

Finance X X X (Gul et al., 2024)

Healthcare

X X

(

Choroszewicz & Alastalo, 2023

)

X X

(

Dai & Shi, 2021

)

Foodservice X X X X X X HR

(

Voi

io et al., 2023

)

Consulting X X (Pemer, 2021)

SME X X X X (Akpan et al., 2022)

Service sector

in general

X X X HR (Cao et al., 2015)

(

Belanche et al., 2019

)

and behaviors of customers (customer response

category) or can be directly perceived by customers

(service offerings, service personalization, and

customer management). The second area consists of

impacts that affect the organization internally.

Hereby, impacts are observable on an operational

(efficiency) level, in a management context

(management/governance, decision-making) or on a

long-term strategic level that affects the entire

organization (innovation, competitive advantages,

monetary impacts).

Across all data analytics techniques that were

investigated, the most frequently reported impacts are

in the area of customer interactions and thus affect

service provision in a way that can be experienced by

customers. Especially articles on big data analytics,

machine learning, and specific analytics methods

show that data analytics positively affects customer

satisfaction, relationship, retention and loyalty, thus

supporting the notion of data-driven customer

relationship management (Jabado & Jallouli, 2024).

This connection is strengthened by the large number

of articles that investigate customer response together

with improved service offerings. The picture is

different in the context of AI where various customer

experiences are the dominant variable being impacted

by data analytics. This is particularly due to the

altered application context of AI which is often

characterized by direct customer interaction such as

robotics (T. Kim et al., 2022) or chatbots (Lui &

Lamb, 2018). This pattern is consistent across the

different industries within the service sector. A more

detailed review of impacts on service offerings shows

that data analytics particularly improves service

quality as well as the way traditional services are

transformed. Examples of service quality

improvement are a higher degree of standardized

high-quality service provision (Wirtz et al., 2023),

superior financial advice of customers (Herrmann &

Masawi, 2022), or enhanced patient care in the

healthcare sector (Dai & Shi, 2021). Customer

management which also results in an improved

interaction between service providers and customers

is primarily influenced by big data analytics, machine

learning, and various specific data analytics methods,

yet less frequently reported in the context of AI. In

contrast, service personalization is associated with all

kinds of data analytics techniques, yet reported in

fewer studies of the sample.

Internally relevant impacts are most frequently

reported in the context of efficiency and management.

Efficiency gains are largely found on an operational

level in all investigated service industries. Data

analytics particularly contributes to improvements of

operational processes, reduction of manual work, and

process control, which are enabled through

innovation (Conti et al., 2024). When it comes to

positive effects on management, articles are

highlighting different areas that are relevant in the

respective industries, such as investment

management in the finance sector (Herrmann &

Masawi, 2022), production process reengineering (Lu

& Xu, 2019), compliance with quality standards in

supply chain management (Khalifa et al., 2021), or

pandemic management in healthcare (Sood et al.,

2022). A notable difference by data analytics

techniques can be found in the context of decision-

making where many articles report positive impacts

of big data analytics and some specific analytics

methods, whereas AI is addressed in only one article

in this respect. This supports the notion that at

present, AI tools are primarily effective in operational

contexts (Hasan et al., 2024), whereas managerial

decision is preferably data-driven (Desgourdes &

Ram, 2024). The improvement of decision-making

through AI systems has been shown in the context of

Business Impacts of Data Analytics in the Service Sector: A Systematic Literature Review

clinical decision-making (Tikhomirov et al., 2024),

however appears to be challenged at present in a

managerial context due to the requirement of

complementary human leadership skills (Duică et al.,

2024).

Interestingly, the number of articles that report

far-reaching, long-term strategic impacts of data

analytics is comparatively low. Out of 70 articles,

only 13 show positive monetary impacts such as

profit increase or cost reduction, 10 report impacts on

competitive advantage, and 5 on innovation. Hereby,

clear deviations between data analytics techniques

can be identified. Whereas monetary impacts are

particularly associated with AI and machine learning,

competitive advantages are more clearly supported by

articles on big data analytics or data analytics in

general, which is consistent with literature that

identified direct (Alshawawreh et al., 2024) or

indirect effects (Rayburn et al., 2024) of big data

analytics on competitive advantage. On the other

hand, the small number of articles that report positive

impacts on innovation do so in the context of big data

analytics or specific analytics methods, but not in the

context of AI and machine learning. The repeatedly

appearing differences in effects of big data analytics

and AI in the service sector support the current

academic debate on specific affordances and

limitations of AI in generating competitive

advantages and innovation. Unlike other information

systems, AI systems at present are characterized by

important limitations in respect of strategic impacts,

such as generic and not idiosyncratic nature, reliance

on explicit knowledge, and a lack of contextual

awareness of processes beyond AI’s assigned tasks

(Kemp, 2024).

6 CONCLUSION

This study investigates empirical evidence revealed

in scholarly literature on the effects of data analytics

in the service sector. The systematic literature review

shows that data analytics provides multiple benefits

to service businesses whereby impacts of different

techniques are varying. Especially big data analytics

and AI-based techniques show different impacts on

customer response as well as effectiveness of

decision-making or operational efficiency. Indirect

impacts on strategic goals such as competitiveness or

innovation were found less frequently, pointing at the

limited scope of effects and difficulties in finding

causal paths between the use of data analytics and

strategic performance.

Like any research, this study shows various

limitations. First, the use of data analytics techniques

has been found to be heterogenous so that a finer

distinction of the effects of specific techniques would

yield more accurate insights. Second, the focus on

English language publications and the exclusion of

lower-ranked publications as well as grey literature

may result in a publication bias and underrepresent

insights that were published outside the investigated

sample. Third, whereas the categorization of effects

was achieved by open coding, a different coding

approach (e.g., based on pre-determined categories

from literature) could have resulted in a different

pattern of impacts.

The findings of this literature review allow for

refining a research agenda on a better understanding

of data analytics and its impact in the service sector.

First, a systematic investigation of different industries

within this sector is necessary to better understand

specific implications of data analytics in different

service contexts. For example, AI tools can provide

different value-added affordances in the healthcare

sector (e.g., in the form of diagnosis) than in

industries with high customer contact (e.g., in the

form of service robots in the hospitality business).

Since some service industries (e.g., real estate,

telecommunications) are under-represented in the

current body of research, more evidence is needed on

such sectors in particular. Second, more research is

needed to better understand the relationships between

identified variables and groups of effects. This could

contribute to clearer insights into the effects of data

analytics on organizations’ performance such as

profitability or competitiveness. Third, the findings

stress the high importance of an interaction between

humans and information systems that are acting upon

data analytics. This affects not only customer

experiences and behavior, but also decision-makers

and other agents in service organizations that are

expected to be increasingly involved in data analytics

in the future.

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