Business Intelligence Solutions Adoption Model for Peruvians SMEs

Based on UTAUT2

Luis Javier Ortiz Leigh

, Axel Gutierrez Huaman

and Jymmy Stuwart Dextre Alarcon

School of Systems and Computer Engineering, Peruvian University of Applied Sciences,

2390 Primavera Avenue, Lima, Peru

Keywords: Adoption, Business Intelligence, SMEs, UTAUT2, Intention to Use.

Abstract: Small and medium-sized enterprises (SMEs) face significant challenges to grow and make strategic decisions

due to their small size and limited resources. This study proposes a model to identify the factors that influence

the adoption of Business Intelligence (BI) solutions in Peruvian SMEs, based on the Unified Theory of

Acceptance and Use of Technology (UTAUT2). The study methodology is divided into four phases. The first

phase consists of analyzing existing technology adoption models to identify critical components that affect

BI adoption. In the second phase, the proposed model is developed and survey questions are designed that

will measure relevant factors for statistical validation. The third phase involves data collection through

surveys targeting SMEs in Peru, followed by analysis to identify significant patterns. The fourth and final

phase validates the model using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique,

evaluating the robustness and accuracy of the proposed model. The validation results show that the proposed

factors Performance Expectation (PE), Price/Value Ratio (PV) and Competitive Pressure (CP) are the most

influential in the intention to use BI solutions by Peruvian SMEs.

1 INTRODUCTION

Small and medium-sized enterprises (SMEs) face a

significant problem with respect to their subsistence:

only 30\% of them manage to survive after 2 years

and the main reason is liquidity (Rubio, 2020). The

causes vary from operating policies, which do not

guarantee survival in the market, often leading

companies to bankruptcy (Roever, 2016), internal

factors (business understanding, resources, budget,

solvency level, liquidity, etc.) and external factors

(political, social, environmental, etc.) (Vukšić, Bach

& Popovič, 2013), trust in one's own experience when

making decisions, ruling out the possibility of opting

to improve this process through the incursion of

technology (Bhatiasevi & Naglis, 2020) and

underestimation of technology, limiting its use to

administrative tasks instead of using it for complex

business operations (Caseiro & Coelho, 2018).

In their study on the intention to use Business

Intelligence in SMEs in Libya using UTAUT2 and

https://orcid.org/0009-0009-5334-4257

https://orcid.org/0009-0009-4740-9253

https://orcid.org/0000-0002-1686-0510

TAM, Alsibhawi, Yahaya & Mohamed (2023)

reveals that the effective adoption of this technology

depends on factors such as business resources and

capabilities. In another study, Kašparová (2023)

shows a technology model based on UTAUT2 for BI

adoption by adding the habit factor within the

constructs of the presented model, which was found

to be the most influential according to the results. On

the other hand, Zheng & Khalid (2022) conducted a

research on ERP and BI adoption in SMEs in

Malaysia using UTAUT and TOE, highlighting that

ERPBI plays an important role in organizational

performance and business continuity, so it is

necessary to adopt it among SMEs. In addition,

Kwarteng, Ntsiful, Diego & Novák (2023) show a

technology model for digitalization adoption in SMEs

in Czech Republic and Slovakia, which highlights the

competitive pressure factor being the most significant

predictor of BI adoption intention in Czech and

Slovak SMEs. Fang, Azmi, Yahya, Sarkan, Sjarif \&

Chuprat (2018) shows their study of mobile BI

Leigh, L., Huaman, A. and Alarcon, J.

Business Intelligence Solutions Adoption Model for Peruvians SMEs Based on UTAUT2.

DOI: 10.5220/0012894800003825

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

In Proceedings of the 20th International Conference on Web Information Systems and Technologies (WEBIST 2024), pages 175-182

ISBN: 978-989-758-718-4; ISSN: 2184-3252

175

adoption based on UTAUT, with adaptation of

TAMMS. The result of the study was that it is

important for users the ease of use and whether it is

useful to have the information at the reach of a mobile

device. However, all these authors have developed

studies on the adoption of BI solutions in different

countries in Asia and Europe, having that in Peru has

not developed a study that evaluates the intention to

use this same technology, so this paper is to show the

proposed model that illustrates the main potential

factors to be influential for the adoption of BI

solutions, based on the UTAUT2 model (Venkatesh,

Thong, & Xu, 2012). This study will focus on

understanding the factors that influence the intention

and acceptance of Peruvian SMEs towards the

implementation of BI solutions, therefore we will

perform (1) Tool selection, (2) Model design, (3)

Data collection and analysis and (4) Validation.

The organization of the article is as follows: in

Section 2, we will talk about the work related to the

proposed solution; in Section 3, about the proposed

model, highlighting all the phases of its development;

in Section 4, about the validation of the proposed

model; in Section 5, about the results of the study and

finally Section 6, with the conclusions of the study, as

well as the possible future work.

2 RELATED WORKS

The field of Business Intelligence (BI) adoption has

gained relevance in response to the growing demand

for data for business decision making. In this context,

previous research is highlighted and will be presented

as related work in this study. These works, conducted

in different countries, share similar models while

incorporating their own constructs, thus offering a

global and contextualized view of the challenges and

strategies in the implementation of BI and other

technologies at the international level.

Rouhani, Ashrafi, Ravasan & Afshari (2018)

conducted an empirical research on the factors

influencing BI adoption in the banking and finance

industry. This research is particularly relevant

because of the few previous attempts to identify the

factors affecting the adoption of BI systems. Given

the rapid proliferation of data in organizations and the

critical importance of managerial decision making, it

is critical to determine the most appropriate factors

for BI adoption, as this significantly influences the

decision to implement BI. In addition, a conceptual

model based on the TOE framework was proposed

and validated using survey data and the PLS

technique. The research concluded that the most

influential factors in IT implementation stages are

grouped into five categories: individual,

organizational, technological, task-related, and

environmental characteristics.

Zheng & Khalid (2022) goes into the adoption of

ERP-BI systems, which should consider

technological, organizational, and environmental

(TOE) factors to ensure its continuity and

sustainability. Since the study aims to close the

current gaps in ERP-BI adoption through a TOE-

focused perspective and presents a conceptual

framework that describes the dimensions of this

theory and its factors. Thus, the article contributes to

the understanding of how companies can successfully

adopt ERP-BI systems in a constantly changing and

competitive business environment, since according to

several authors it has been shown that the integration

of ERP and BI systems enhances business decision

making capabilities. What refers to our study, what is

different is that it is only based on measuring the

intention to use BI solutions, using UTAUT2.

Bany Mohammad, Al-Okaily, Al-Majali &

Masa’deh (2022) examined the relationship between

business intelligence capabilities and the

performance of SMEs in Jordan, under the TOE

framework, taking into account the moderating role

of competitive intelligence. The results showed that

BI capabilities have a significant influence on

business performance and that competitive

intelligence positively enhances this relationship.

This indicates that SMEs should focus on developing

their BI capabilities to improve their competitiveness

in the marketplace. However, although this study

focused on measuring BI usage intention in Jordanian

SMEs, the study we propose is based on the UTAUT2

model, which includes more specific constructs

related to software usage and acceptance.

Ahmad, Miskon, Alabdan, & Tlili (2021) a BI

system adoption model based on the TOE framework

was investigated in the textile industry, extending the

original model with an additional dimension

(Individual) in addition to the three traditional

dimensions (technological, organizational, and

environmental). Preliminary findings from the

interviews were used to validate the suitability of the

proposed model. The research model was then

validated through a questionnaire survey and

DEMATEL techniques. Analysis of the results

indicated that user traits, technological maturity,

sustainability, leadership commitment and support,

and compatibility were the most significant factors.

The determinants of the causal group were shown to

have greater influence throughout the model

compared to those of the effect group, such as

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interpersonal communications and satisfaction with

existing systems. In relation to our study, we

highlight the use of the TOE framework to measure

BI usage intention in the textile industry, highlighting

the flexibility of the model to be applied in different

industries.

Hmoud, Al-Adwan, Horani, Yaseen & Al Zoubi

(2023) made a study based on the Technology-

Organization-Environment model, which provides a

comprehensive framework for understanding

technology adoption processes. By applying the TOE

model specifically to the context of BI adoption in

Educational Institutions, this study extends the

theoretical understanding by incorporating

organizational and environmental factors unique to

Jordanian HEIs. One of the main contributions of this

study is its focus on BI adoption by Educational

Institutions in Jordan, filling an important gap in the

literature by identifying and analyzing the factors that

influence this adoption. The findings show that

organizational characteristics, such as top

management support, information culture,

complexity, and vendor selection, have a positive

impact on BI adoption in these institutions. This study

is significant in that it uses the context of Jordanian

educational institutions to demonstrate that it is

possible to measure the intention to use any

technology. We use the UTAUT2 model, which is

more oriented to measure acceptance with constructs

specific to software use.

3 PROPOSED MODEL

The following are the parts of this study: first, the

selection of tools was carried out, then the proposed

model was designed, followed by data collection and

analysis, and finally, the validation of the results.

Figure 1: Proposed Metholodogy.

3.1 Phase 1: Tool Selection

The goal of this phase is to be able to adequately

determine the tools that best fit the proposal proposed

in this study to be used and applied. To this end, we

first conducted a systematic review of the literature

and then benchmarked the models used in the articles

reviewed. Regarding the review, this allowed us to

define the criteria to be taken into account for the

evaluation and comparison of the selected models:

 Focus: This aspect refers to the main focus or

central issue addressed by each model.

 Key Factors: These are the main factors or

constructs that each model considers as

determinants of technology adoption.

 Main Application: Indicates in which areas or

contexts each technology adoption model is

commonly applied.

 Integration of Social Factors: This aspect refers

to the consideration of social factors and social

influences on technology adoption within each

model.

Once the articles were compiled, a benchmarking

of the models used in the studies was carried out,

highlighting the model approach, the key factors

used, the main application of the model used and

finally the integration of social factors, which is

shown in the following table:

Table 1: Comparative table of adoption models according

to comparison criteria.

From the benchmarking, the UTAUT2 model was

chosen, as it provides more value and its approach fits

our study. Also, Quicano (2019) demonstrates that the

Phase 1: Tool

Selection

• Benchmarking of

existing models

Phase 2:

Model design

• Model construction, survey

design for validation and

definition of statistical

metrics for outcome

evaluation.

Phase 3: Data

collection and

analysis

• Application of the

validation survey of

the proposed model

factors.

Phase 4:

Model

validation

• Modeling of the data

collected for the

validation of the

proposed model.

Business Intelligence Solutions Adoption Model for Peruvians SMEs Based on UTAUT2

177

UTAUT2 model provides superior explanatory

power compared to other technology acceptance

models. By incorporating additional factors such as

hedonic motivation, price value, and habit, UTAUT2

more accurately captures the complexities of user

acceptance and usage behavior, outperforming its

predecessors in predictive capability. In addition, an

additional construct from a peer-reviewed article is

incorporated, which contributes significantly to the

result of this article.

3.2 Model Design

The objective of this phase is to design the proposed

model based on the selected tools. To this end, the

model was first built, the survey design was defined

and the metrics for the validity of the model were

defined. First, the constructs of the model were

analyzed, as well as the external construct identified

during phase 1. From this, the proposed model was

designed, which contemplates some constructs of the

UTAUT2 model, adding the identified external

construct.

Figure 2: Proposed Model.

The proposed model has the following factors:

• Performance Expectancy: Performance

expectancy refers to the individual’s

perception of whether using a particular

technology will improve his or her job or

personal performance.

• Effort Expectancy: Effort expectancy refers

to the individual’s perception of the

perceived ease or difficulty of using a

particular technology.

• Social Influence: Social influence refers to

the individual’s perception of the perceived

social pressure to use a particular

technology.

• Facilitating Conditions: Facilitating

conditions refer to additional resources that

can help make the adoption of a technology

easier and more successful.

• Hedonic Motivation: Hedonic motivation

refers to the degree to which a person

perceives that using a particular technology

will provide pleasure, fun, or entertainment.

• Price/Value Ratio: Price/value ratio refers to

an individual’s perception of whether the

cost of acquiring and using a technology is

justified by the benefits and value it

provides.

• Habit: Habit refers to the degree to which an

individual has a habit or routine of using a

particular technology.

• Competitive Pressure: refers to the forces

and challenges organizations face to remain

competitive in a dynamic and changing

environment.

For the validation of the model, a questionnaire with

questions that are directly related to each construct

will be carried out. Below is the table with the

questions for each construct:

Table 2: Questionnaire for the validation of the model.

Construc

ID Question

Performance

Expectancy

(Venkatesh,

Thong, & Xu,

2012)

ED1 Digitization would be useful in

my work.

ED2 Using digitized processes allows

me and the company to perform

tasks efficiently and quickly.

ED3 The use of digitized processes

and services increases

productivity

ED5 Digitalization impacts profits and

performance in the company

ED6 My good digital skills increase

my chances of getting a raise

Effort

Expectancy

(Venkatesh,

Thong, & Xu,

2012)

EE1 My interaction with the digitized

work environment would be clear

and understandable

EE2 It would be easy for me to

acquire digital skills to work in

the digitized work environmen

EE3 You would find a digitized work

environment easy to use

EE4 Learning how to operate

processes in a digitized way is

easy for me

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able 2: Questionnaire for the validation of the model

(cont.).

Facilitating

conditions

(Venkatesh,

Thong, & Xu,

2012)

CF1 The company has the resources to

use more digitized processes and

services.

CF2 The company has the necessary

knowledge to use more digitized

processes and services

CF3 Modern digitalization techniques

are not compatible with other

digitized processes and services

in the company

CF4 A specific person (or group) is

available to help if difficulties

arise with digitalization in the

company.

Social Influence

(Venkatesh,

Thong, & Xu,

2012)

IS1 The people who are important to

me think that I should use

digitization tools in my work.

IS2 My work environment

(colleagues, bosses, etc.) thinks I

should use digitization tools.

Motivation

(Venkatesh,

Thong, & Xu,

2012)

ME1 Using digitizing tools is fun

ME2 Using digitization tools is nice

Price/Value

Ratio

(Venkatesh,

Thong, & Xu,

2012)

PV1 Digitization tools are good value

for money.

SS2 Digitization tools offer good

value for money.

Habit

(Venkatesh,

Thong, & Xu,

2012)

HA1 Using digitization tools has

ecome a habit for me.

HA2 I feel like I'm in the habit of using

digitizing tools.

Competitive

pressure

(Kwarteng et.

al., 2023)

CP1 My company plans to invest more

in digitalization in the future.

CP2 Business partners who are

important to the company think

that the company should be more

digitized.

Behavorial

intentention

(Venkatesh,

Thong, & Xu,

2012)

IU1 My company intends to digitize

its business processes to a greater

extent.

IU2 My company predicts that it will

introduce more digitalization in

the near future.

IU3 My company plans to invest in

more digitalization in the future.

This survey includes questions related to each

construct, so that respondents can give their opinion

and find out which one has the greatest influence on

their ability to adopt BI solutions. The answers are

based on a Likert scale, so as not to limit them to only

“yes” or “no”. This will be developed in a Microsoft

Forms questionnaire, since it will be via mail that the

selected SMEs will be able to complete the survey.

As for the validation metrics, we have Cronbach's

alpha, which is the degree of reliability that reflects a

given content domain of what is measured (George &

Mallery, 1995). This value must pass 0.7 to have

acceptable reliability. For hypothesis testing, we have

the following metrics: Convergent Validity, which

must be greater than 0.70 to be acceptable;

Discriminant Validity (X2) which must be less than

0.05 and Average Variance Extracted (AVE) which

must be greater than or equal to 0.50 [18]. For the fit

of the measurement model, we have the normalized

Chi-square (CMIN/DF) which should be 1<X2/df<5;

Goodness of fit (GFI) which should be greater than

0.9; Comparative Fit (CFI) greater than 0.9 and Root

mean square error of approximation (RMSEA) < 0.08

(Gutarra, 2012).

3.3 Data Collection and Analysis

For this phase, the goal is to define the sample to

which the survey of phase 2 will be applied, and then,

once applied, to collect the data and analyze them

before the validation of the proposed model.

For the selection of companies for the sample, the

government's open databases will be searched in

order to collect the largest number of candidate

companies, and by means of a simple sampling, the

sample to be taken to apply the survey will be defined.

These companies will be validated with respect to

their business segment, since they must belong to the

SME segment. Then, once the survey has been

applied to the selected sample, the data from the

responses will be collected and cleaned, in order to

have a clean data set ready to be processed.

Finally, a descriptive analysis of the results of the

survey will be made, with the objective of rescuing

insights from the SMEs, to be taken into account

when drawing conclusions regarding the model

calibrated after the modeling.

3.4 Phase 4: Model Validation

The objective of this phase is to validate the proposed

model based on the surveys applied to the selected

companies. For this purpose, the variables will be

modeled using the PLS-SEM method (Structural

Equation Modeling).

The use of the PLS-SEM method is based on its

ability to handle complex models, even with small

samples and data that do not meet normality

assumptions. It is less restrictive than other

techniques, such as Covariance-Based Structural

Equation Modeling (CB-SEM), and is particularly

useful for exploratory and predictive research. Hair et

al. (2019) mentions that PLS-SEM is a flexible and

powerful tool that allows for the simultaneous

evaluation of multiple relationships between latent

Business Intelligence Solutions Adoption Model for Peruvians SMEs Based on UTAUT2

179

variables in contexts where other techniques might

fail.

Once all the survey results are available, structural

equation modeling is performed to analyze the

relationships of the variables (the constructs) and to

see which are the most influential factors for the

intention to use. PLS-SEM is a statistical technique

used to analyze the relationships between latent

(unobservable) variables in a model.

With this, we would finally have the model

adjusted to the context of Peruvian SMEs, showing

the survey questions with their respective weights,

which will reveal the construct that most influences

SMEs in their intention to adopt BI solutions. In

addition, it can be discussed whether the UTAUT2

model is the most efficient to use in the context of

Peruvian SMEs or not, being able to recommend

other constructs to enrich such theory and thus

improve the literature of this type of study.

4 VALIDATION

For the validation of the proposed model, the

following phases were carried out: (1) survey design,

(2) selection of participating companies, (3) sending

the survey, (4) data collection and analysis, and (5)

evaluation of the model.

For the survey design, questions related to each

construct of the model were asked using a Likert scale

(from 1 to 5) (see table 2). For the selection of

participating companies, a total of 130 companies in

the SME sector from all regions of Peru were

selected. On the other hand, to send the survey, it is

in a Windows Forms form, which is sent by e-mail to

the selected companies. Once all the responses are

received, they are collected and loaded into the IBM

SPSS Statistics software, in order to group them so

that they can be loaded into the SmartPLS modeling

software.

Finally, once the information has been uploaded

to the modeling software, hypothesis testing is

performed with the constructs of the model. The

model will be validated with statistical metrics.

All 130 SMEs responded to the survey. It was sent

to the managers of each company to get an overview

of their views on the adoption of BI in their

businesses. The survey was available for one week

after the emails were sent. After the survey deadline,

the data was collected, initially exported to an Excel

format, then loaded into SPSS to be formatted

properly before being uploaded without issues to

SmartPLS. Since all the questions were mandatory,

there were no unanswered questions.

5 RESULTS

After processing the survey data and adjusting the

model to acceptable thresholds for the metrics, we

have that the final observable variables of the model

(which would be the valid hypotheses of the influence

of intention to use BI solutions) are: Performance

Expectation (PE), Price/Value Ratio (PV) and

Competitive Pressure (CP).

Table 3: Hypothesis test results.

Hypothesis Estimate S.E C.R. P-

Value

Results

H1 (PE-> BI) 0,343 0.083 3.996 0.001 Pass

H6 (PV->BI) 0,462 0.088 4.126 0.001 Pass

H8 (PC->BI) 1,045 0.079 12.874 0.001 Pass

The factors pass based on their strong and statistically

significant metrics. For each hypothesis, the high

Critical Ratios (C.R.) and low P-Values (all below

0.05) indicate robust relationships between the

variables. The path coefficients (Estimates) show

positive and substantial effects, with perceived cost

(PC) having the strongest influence, as reflected by its

high estimate (1.045). These metrics confirm that

performance expectancy (PE), price/value ratio (PV),

and competitive pressure (CP) significantly affect

behavioral intention (BI), validating the hypotheses

and demonstrating that the relationships are not due

to chance.

Table 4: Model fit indices.

Model fit indices Final SEM

Índex Threshold Index Fulfillment

Normalized

Chi-square

>1 y <5 2,96 Pass

GFI >0,9 0,92 Pass

CFI >0,9 0,96 Pass

RMSEA <0,08 0,078 Pass

Such variables have factor loadings greater than 0.7

(acceptable level), as well as their variances are

greater than 0.5. With this, it can be concluded that

the model already has a conforming and validated

structure.

6 CONCLUSIONS

The present study allowed us to explore the factors

influencing the intention to use Business Intelligence

(BI) solutions in small and medium-sized enterprises

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(SMEs) in Peru, based on a survey. Information was

collected from 130 SMEs across Peru to validate the

UTAUT2 model.

Through detailed analysis, three main constructs

were identified that exert significant influence on this

intention: Performance Expectancy (PE), Price/Value

Ratio (PV), and Competitive Pressure (CP). Thus, it

is found that in the UTAUT2 model, only PE and PV

are valid in the analyzed context, as well as CP.

However, while Performance Expectancy is

recognized as a critical factor for SMEs across

various countries, including the Czech Republic

(Kašparová, 2023), additional factors such as PV and

CP may gain prominence depending on the specific

market in which these SMEs operate. This suggests

that while PE remains universally important, the

impact of other constructs can vary significantly

based on the unique characteristics of different

geographical and cultural contexts.

Together, these findings provided valuable

insights for both SMEs considering the adoption of

BI solutions and for the providers of these

technologies. For SMEs, it is crucial to assess how BI

solutions can integrate and enhance their operations

effectively. For providers, understanding these

motivations can help develop more effective

marketing and sales strategies, aligning their

offerings with the specific needs and concerns of

SMEs.

Future studies could further explore how other

factors, such as staff training and technical support,

influence the adoption of BI solutions or other

essential solutions for the optimization and growth of

SMEs. Specifically, it would be valuable to conduct

an analysis focused on the retail sector, given its

dynamism and high competitiveness, to better

understand how SMEs in this industry perceive and

utilize BI technologies.

It is important to note that the findings of this

study are based on the context of Peruvian SMEs.

Due to differences in economic, social, and

technological contexts between countries, the results

may not be generalizable to SMEs in other regions.

The specific conditions of each market can influence

the identified technology acceptance factors.

ACKNOWLEDGMENTS

We would like to express our most sincere gratitude

to the School of Systems and Computer Engineering

of the Peruvian University of Applied Sciences for

providing us with the necessary tools and

environment for the development of this research. To

our advisors, for their invaluable guidance, patience

and support throughout this project. Finally, to the

other individuals who were involved in the

development of the project, for their constant

encouragement, understanding and unconditional

support, without whom this achievement would not

have been possible.

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