Bridging the Gap in Agricultural Sharing Economy: A Systematic

Review for Evaluating Information Systems

for Machinery Efﬁciency

Reinaldo Wendt

, Eduardo Tiadoro

, Fabio Basso

and Maicon Bernardino

Postgraduate Program in Software Engineering (PPGES),

Laboratory of Empirical Studies in Software Engineering (LESSE),

Federal University of Pampa (UNIPAMPA) - Av. Tiarajú, 810 - Ibirapuitã, CEP 97546-550 - Alegrete - RS, Brazil

Keywords:

Sharing Economy, Agricultural Sector, Machinery Rental, Machinery Sales, Gray Literature.

Abstract:

The sharing economy is rapidly transforming various industries, including agriculture, where there is growing

demand for systems that facilitate machinery rental and sales. Agricultural machinery is often expensive and

is used primarily during speciﬁc periods, such as harvests. This limited utilization leads to high depreciation

costs, imposing substantial and scalable ﬁnancial burdens on owners. This study investigates how a sharing

economy model can improve the efﬁciency of agricultural machinery use. By allowing equipment owners to

maximize utilization and providing small-scale farmers with affordable access to machinery, such a model

reduces the need for signiﬁcant upfront investments. We conducted a qualitative analysis to evaluate the effec-

tiveness of current information systems that support this approach. The methodology involved exploring grey

literature to identify relevant tools, deﬁning evaluation criteria, and conducting a qualitative assessment of

existing platforms. Among 14 evaluated platforms, we rated only four as acceptable, with only one achieving

a good rating. None fully met all the criteria, revealing a gap between user needs and the solutions currently

available in the market. This study highlights the inadequacies in existing platforms and offers valuable in-

sights for advancing the sharing economy in agriculture. By identifying speciﬁc needs and challenges, the

ﬁndings provide a foundation for future research and the development of more effective technologies and

practices in this domain.

1 INTRODUCTION

With the pursuit of more efﬁcient and sustainable food

production, it has become necessary to optimize agri-

cultural processes. Emerging technologies are already

part of the reality in this ﬁeld, as demonstrated by Al-

biero et al. (2020). However, access to innovations

from previous generations, such as autonomous ma-

chines, is still limited (Sordi and Vaz, 2020). Further-

more, Neves et al. (2009) emphasize that there is a

limiting economic factor associated with the idleness

of this machinery in certain properties.

In this scenario, the sharing economy emerges as

a promising solution. According to Hamari et al.

(2015), it is a recent phenomenon that promotes the

https://orcid.org/0009-0007-4940-2261

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https://orcid.org/0000-0003-2776-8020

direct transfer of goods and services between peo-

ple, supported by digital technologies. The authors

link this reality to ICT (Information and Communi-

cation Technologies) informatization, highlighting its

core aspects. Key traits are: online collaboration, in-

volving decentralized digital content creation and use;

social commerce, enabling peer-to-peer interactions

for buying and selling via social networks; and online

sharing, covering information exchange and service

offerings through ICTs.

As pointed out by Zanchett et al. (2018), the de-

velopment of agricultural activities requires the use

of high-cost machine and equipment for acquisition

and maintenance. Furthermore, the use of these tech-

nologies is limited to harvest periods, remaining idle

for most of the time. This results in high depreciation

costs, which can translate into a scalable expense over

time for the owners.

Given this, the authors emphasize that one way

to improve the scenario of efﬁciency in the use of

320

Wendt, R., Tiadoro, E., Basso, F. and Bernardino, M.

Bridging the Gap in Agricultural Sharing Economy: A Systematic Review for Evaluating Information Systems for Machinery Efﬁciency.

DOI: 10.5220/0013429400003929

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 2, pages 320-327

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

agricultural machinery could be the sharing economy

model (Belk, 2014). This model allows owners to

maximize the use of their equipment, while users can

meet their needs with a lower investment than would

be required to acquire the machinery. In other words,

the sharing economy, by enabling the rental or sale of

machinery, allows owners to maximize the use of their

equipment and enables farmers with fewer resources

to access essential technologies with a smaller invest-

ment. Despite advances in informatization, there are

still signiﬁcant gaps in meeting the needs of users in

this market.

Furthermore, this study is based on the Diffusion

of Innovations Theory (Rogers, 2003) to understand

the factors that inﬂuence the adoption of innovative

tools in the agricultural context. According to this

theory, the adoption of an innovation depends on fac-

tors such as the perception of its relative advantage,

compatibility with the values and needs of users, sim-

plicity of use, and observability of its beneﬁts. In the

agricultural sector, the theory provides a useful frame-

work for analyzing the challenges and opportunities

associated with the dissemination of sharing economy

platforms. Works such as those by Moore and Ben-

basat emphasize the importance of adapting innova-

tive functionalities to the speciﬁc needs of users to

facilitate their adoption.

Given the scenario presented, this study aims to

raise and conduct a qualitative analysis of sharing

economy platforms focused on advertising the rental

and sale of agricultural machinery. In other words,

the present study seeks to qualitatively analyze shar-

ing economy platforms focused on the rental and sale

of agricultural machinery. Through a search in the

gray literature, we identiﬁed fourteen (14) tools, each

with distinct business models and speciﬁc function-

alities. Then, we conducted a brainstorming process

to deﬁne relevant criteria, which were weighted us-

ing the planning poker method. With these criteria

established, we carried out a qualitative evaluation of

the platforms, assigning scores based on the weighted

sum of the criteria by the quality of implementation

of each tool. This work aims not only to highlight the

strengths and weaknesses of these platforms but also

to inspire the development of more effective techno-

logical solutions aligned with the sector’s demands.

The remainder of the study is structured as fol-

lows: Section 2 presents related works. Next, Section

3 describes the methods used to achieve the objec-

tives of this study. Section 4 presents the tools found,

the evaluation results, and the analysis of the obtained

ﬁndings. Then, in Section 6, we describe the threats to

the validity of this study. Finally, Section 9 provides

the concluding remarks on the article and potential fu-

ture work on the topic.

2 RELATED WORK

This section aims to discuss related works in research

or the development of platforms that are related to the

sharing economy applied to the context of agricultural

machinery.

Zanchett et al. (2018) investigated the existing

agricultural machinery and equipment sharing soft-

ware on the market. To do so, they conducted an

exploratory study addressing the topic qualitatively.

Through a search based on a systematic mapping, the

systems were listed and evaluated. In the end, the

authors developed a description of the listed applica-

tions, specifying their basic features, strengths, differ-

entiators, and weaknesses.

Nunes (2023) carries out the software engineer-

ing process for the production and validation of proto-

types of an agricultural machinery rental application.

To do so, the author ﬁrst speciﬁes the system require-

ments. Based on what was generated, the system pro-

totypes are developed. Finally, the author validates

the artifacts, including a usability evaluation variation

intended for prototypes.

Furthermore, this work can also serve as a relevant

reference for future research and developments in the

ﬁeld of the sharing economy applied to the agricul-

tural context. By conducting the qualitative analysis

of existing platforms, our research contributes to an

understanding of the needs and challenges in this do-

main. In this way, we hope that the results presented

here can complement and inspire new studies aimed

at advancing practices and technologies in this sector.

The study aimed to develop AgroShare (Da Silva

et al., 2024), a platform for sharing and trading agri-

cultural resources, addressing inefﬁciencies and ﬁ-

nancial constraints among small-scale farmers. It of-

fers features like resource listings, messaging, and

rental agreements. A usability test conﬁrmed its user-

friendly interface and practical functionality but high-

lighted areas for improvement, such as search work-

ﬂows and advanced recommendations. The study

concluded that AgroShare promotes sustainable and

inclusive farming practices while offering insights for

enhancing digital agricultural tools.

These works contribute in distinct and signiﬁcant

ways to the understanding and development of our

work. In Table 1, a summary of the main characteris-

tics addressed by each of these references can be seen.

Bridging the Gap in Agricultural Sharing Economy: A Systematic Review for Evaluating Information Systems for Machinery Efﬁciency

321

Table 1: Comparison of Related Works.

Reference Main Objective Methodology Results and Conclusions

Zanchett et

al. (2018)

Investigation of agricultural ma-

chinery sharing software

Exploratory study, system-

atic mapping

Identiﬁcation of positive and neg-

ative aspects of the analyzed sys-

tems

Nunes

(2023)

Development and validation of

prototypes for agricultural ma-

chinery rental apps

Software engineering, re-

quirements speciﬁcation,

prototyping

Creation of prototypes with good

usability acceptance by users

Da Silva et

al. (2024)

AgroShare is a platform for

sharing and trading agricultural

resources addressing small-scale

farmers

Usability test with 47 par-

ticipants to evaluate user-

friendly interface and practi-

cal functionality

Promotes sustainable and inclusive

farming practices while offering

insights for enhancing digital agri-

cultural tools

Our Study Qualitative analysis of sharing

economy platforms applied to the

agricultural context

Qualitative study, compara-

tive analysis of platforms

Identiﬁcation of the sector’s cur-

rent needs and suggestions for fu-

ture research

3 METHODOLOGY

According to Garousi et al. (2019), gray literature

refers to documents and materials not formally pub-

lished through traditional academic channels, such as

peer-reviewed articles and books. It includes a wide

range of sources, such as technical reports, work-

ing papers, theses, specialized websites, and disser-

tations. These sources often contain valuable infor-

mation that is not available through conventional aca-

demic methods and can provide important data on

current practices, recent developments, and emerg-

ing techniques. The decision to prioritize gray liter-

ature was driven by the limited availability of peer-

reviewed academic research speciﬁcally focused on

sharing economy platforms for agricultural machin-

ery. Additionally, gray literature, such as technical

reports and industry websites, often provides more

practical insights into platform functionalities.

Research Objectives: Consisted the ﬁrst step,

which aimed to collect information about platforms

for agricultural machinery ads and map their func-

tionalities. The goal was to identify which of these

platforms could be converted into features for a soft-

ware. Additionally, the aim was to discover which

platforms are currently being used by farmers. Thus,

Figure 1 formalizes the research method through the

following Research Questions (RQ):

RQ1. What advertising platforms for

agricultural machinery exist?

RQ2. What are the features of the

found platforms?

Figure 1: Research Questions.

3.1 Search and Selection of Tools

For the search of the software, we developed a string

that was submitted to the Google search engine. We

composed the string of the following elements: (i) the

general theme, addressing agriculture and related

terms such as “agro” and “farm”; (ii) the subtopic,

focused on machinery, including terms like “machin-

ery” and “equipment”; (iii) the objective, covering

sharing economy and transactions, with words like

“rent”, “used”, and “purchase”; (iv) keywords related

to solutions, such as “platform”, “system”, “soft-

ware”, and “website”.

Figure 2 shown the string used in the search.

(agro OR farm OR agricultura OR rural

OR agrícola OR agronegócio) AND

(machine OR machinery OR tractor OR

máquina OR máquinas OR maquinário OR

equipamento) AND (rent OR rental OR

used OR buy OR announcement OR

aluguel OR alugar OR usado OR comprar

OR loja OR anúncio) AND (platform OR

plataforma OR sistema OR software OR

site OR portal)

Figure 2: Search String of the Research.

After submitting the search string to the search

engine, we conducted an initial screening by review-

ing the tool descriptions and key functionalities avail-

able on their landing pages. This assessment aimed

to quickly identify whether each tool aligned with

the study’s scope and objectives. Based on this ini-

tial analysis, we selected the most suitable tools for a

more detailed evaluation, focusing on those that met

the established criteria.

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3.2 Evaluation Criteria

The evaluation criteria were established during a

brainstorming session among three evaluators (co-

authors), focusing on the functionalities we consid-

ered essential for an agricultural machinery rental sys-

tem. To assign weights to these criteria, we used the

Planning Poker technique, a collaborative methodol-

ogy that promotes the active participation of the eval-

uators to reach a consensus.

Each evaluator suggested a value from 1 to 5

for each criterion, where one (1) represents low pri-

ority and ﬁve (5) represents high priority. If the

proposed values were too divergent, the evaluators

discussed the differences until reaching a consensus

on the most appropriate weight. As described by

Grenning, J. (2002), Planning Poker is an efﬁcient

technique that facilitates collaboration and decision-

making by structuring the team estimation process.

Table 2 shows the criteria raised with their weights.

Table 2: Criteria and Assigned Weights.

ID Criteria Weight

a Machine Rental 5

b Service Rental 5

c Filtered Search Interface 5

d Platform Internal Chat 4

e Filtering Based on Technical Spec-

iﬁcations

f Rental History and Other Reports 4

g Rating and Review System for Ads 4

h Price Alert 3

i Price and Feature Comparison 3

j Machine Purchase 3

k Filtering Based on Geographic and

Financial Conditions

l Stores 3

m Proximity Search 2

n Wish List 2

o Location Map 2

3.3 Qualitative Evaluation

This study used a modiﬁed four-point Likert

scale (Likert, 1932) to assess the functionality of the

website, avoiding neutral responses, as recommended

by Clason and Dormody (1994) (Clason and Dor-

mody, 1994).

Instead of “agree” or “disagree”, evaluators as-

signed qualitative scores: “not implemented”, “par-

tially implemented”, “implemented,” and “fully im-

plemented”. This adaptation enabled a more precise

evaluation of usability and technical functionality.

Multiplication factors were deﬁned to reﬂect the

quality of implementation, ensuring a fair and mea-

surable evaluation. They capture the gradation from

absent to ideal implementations, rewarding superior

systems with higher scores. Table 3 presents the im-

plementation levels and their respective factors.

The score ranges were established based on the

weighted sum of the products between the criteria and

their respective maximum implementation weights.

To calculate the maximum possible score, we used the

following formula:

Score = ((5 × 3) +(4 × 4) + (3 × 5) + (2 × 3)) × 1.5 = 78

(1)

This value represents the ideal performance across

all criteria, considering the implementation highest

level. From this maximum score, we created ﬁve clas-

siﬁcation ranges for the evaluated systems: (i) Inade-

quate, score from 0 to 15.6; (ii) Mediocre, from 15.7

to 31.2; (iii) Acceptable, from 31.3 to 46.8; (iv) Good,

from 46.9 to 62.4; (v) Ideal, from 62.5 to 78.

4 RESULTS

Throughout this section, we present the results of

our research obtained through the application of the

methodology’s procedures. Initially, we present the

tools found through the search in the gray literature.

Next, we show the results of the qualitative evaluation

conducted on the identiﬁed platforms, using the crite-

ria and implementation levels previously established.

4.1 Tools Found

This section presents the platforms identiﬁed from the

search in the gray literature. Table 4 lists the tools

found, detailing their business model, corresponding

URL, and the region of operation. Figure 3 shows

the distribution of tools by business model, with the

majority of 11 (78.6%) tools focused on Purchases.

Rent

Purchase

Auction

2/14.3% 2/14.3%

7/50%

2/14.3%

1/7.1%

Figure 3: Distribution of Business Models.

Bridging the Gap in Agricultural Sharing Economy: A Systematic Review for Evaluating Information Systems for Machinery Efﬁciency

323

Table 3: Implementation Levels.

Level Description Factor

⊗ The system does not provide the functionality or feature evaluated by the criterion 0x

⊘ The system has the evaluated functionality, but it is implemented in a limited or unsatisfactory way 0.5x

⊙ The system adequately implements the functionality 1x

⋆ The system provides the functionality in an excellent manner 1.5x

Legend: ⊗: Not Implemented | ⊘: Partially Implemented | ⊙: Implemented | ⋆: Fully Implemented

Table 4: List of Advertising Platforms for Agricultural Ma-

chinery.

Tools Site Region

Agrofy agrofy.com.br

AgroLiga agroliga.com.br

Agriaaires agriaffaires.com.br

Alluagro alluagro.com.br

E-agro e-agro.com.br

E-FARM e-farm.com

Iron Planet ironplanet.com

MachineFinder machinefinder.com

Machinery Pete machinerypete.com

Machinio machinio.com.br

Mascus mascus.com.br

MFrural mfrural.com.br

Tractor House tractorhouse.com

Tractor Zoom tractorzoom.com

Legend: Business Model | Rent | Purchase | Auction

4.2 Evaluation of the Tools

We evaluated each tool based on the criteria (Table 2).

The score assigned reﬂects the overall performance of

the platform concerning these criteria. The aim is to

highlight the strengths and weaknesses of each tool,

providing a clear view of its effectiveness in the con-

text of the shared economy for agricultural machinery.

Table 5 presents the ﬁnal view of the qualitative evalu-

ation, which results from the grouping and discussion

of the evaluations among three team members.

5 ANALYSIS OF THE RESULTS

In this section, we analyze the results of the platform

evaluation, addressing the following aspects: (i) a

general comparison of the performance of the evalu-

ated platforms; (ii) the overall implementation of the

most important criteria; (iii) the least met criteria.

5.1 General Comparison of Platforms

Platform performance varied signiﬁcantly. Agriaf-

faires scored highest (49.5), excelling in most crite-

ria, while AgroLiga and Mascus had the lowest scores

(13), with poor implementation.

Most platforms scored between 20 and 30 points.

Agrofy (38.5) and Tractor House (35) performed

well, standing out despite ranking below the top plat-

form. Figure 4 shows a pie chart of the classiﬁcation

distribution.

Good System

Acceptable System

Mediocre System

Inadequate System

2 (14.3%)

7 (50.0%)

4 (28.6%)

1 (7.1%)

Figure 4: Distribution of Platform Ratings.

5.2 Implementation of Key Criteria

Criterion a (machine rental) was implemented by only

four platforms (Agrofy, AgroLiga, Afriaffaires, and

Alluagro). In contrast, criterion c (ﬁlter-based search)

showed consistent adoption across platforms, making

it one of the most important. Criterion e (ﬁltering by

technical speciﬁcations) also had good adherence, as

it is a key search mechanism.

However, key criteria such as b (service rental), d

(internal chat), f (rental history and reports), and g (ad

evaluation and comments) were rarely implemented.

Their absence or inadequate implementation limits

user experience and platform effectiveness. Figure 5

presents a chart on the implementation of these crite-

ria (a to g), weighted between 4 and 5 points.

5.3 Least Addressed Criteria

The least addressed criteria by the evaluated platforms

were: (i) the rental of services, with only two im-

plementations; (ii) the internal chat feature, with no

implementations; (iii) the rental history and report-

ing mechanism, with no implementations; (iv) and the

feedback and comment system, with only two imple-

mentations. This highlights signiﬁcant gaps in user

experience and platform efﬁciency.

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

324

Table 5: Evaluation of Advertising Platforms for Agricultural Machinery.

Tools a b c d e f g h i j k l m n o Score

Agrofy ⋆ ⊘ ⋆ ⊗ ⊙ ⊗ ⊗ ⊗ ⊗ ⋆ ⊙ ⋆ ⊙ ⋆ ⊗ 38.5

AgroLiga ⋆ ⊗ ⊘ ⊗ ⊗ ⊗ ⊗ ⊗ ⊗ ⊗ ⊙ ⊗ ⊗ ⊗ ⊗ 13.0

Agriaffaires ⋆ ⊗ ⋆ ⊗ ⋆ ⊗ ⊗ ⋆ ⋆ ⋆ ⋆ ⊘ ⋆ ⋆ ⋆ 49.5

Alluagro ⋆ ⊙ ⊙ ⊗ ⊘ ⊗ ⊙ ⊗ ⊗ ⊗ ⊙ ⊘ ⊙ ⊗ ⊗ 30.0

E-agro ⊗ ⊗ ⋆ ⊗ ⋆ ⊗ ⊗ ⊗ ⊗ ⋆ ⊘ ⊗ ⊘ ⊗ ⊗ 25.0

E-FARM ⊗ ⊗ ⋆ ⊗ ⋆ ⊗ ⊗ ⊗ ⋆ ⋆ ⋆ ⊗ ⋆ ⊗ ⋆ 33.0

Iron Planet ⊗ ⊗ ⋆ ⊗ ⊘ ⊗ ⊗ ⊗ ⊙ ⋆ ⋆ ⊗ ⊙ ⋆ ⋆ 29.5

MachineFinder ⊗ ⊗ ⊙ ⊗ ⊙ ⊗ ⊗ ⊗ ⊗ ⊙ ⊙ ⊙ ⋆ ⋆ ⊙ 26.0

Machinery Pete ⊗ ⊗ ⊙ ⊗ ⊙ ⊗ ⊗ ⊗ ⊗ ⊙ ⊙ ⋆ ⊙ ⋆ ⊗ 24.5

Machinio ⊗ ⊗ ⋆ ⊗ ⋆ ⊗ ⋆ ⊘ ⊗ ⋆ ⋆ ⊗ ⊗ ⊗ ⊗ 30.0

Mascus ⊗ ⊗ ⋆ ⊗ ⋆ ⊗ ⊗ ⊗ ⊗ ⋆ ⋆ ⊙ ⊗ ⊙ ⊗ 27.5

MFrural ⊗ ⊗ ⊘ ⊗ ⊗ ⊗ ⊗ ⊗ ⊗ ⋆ ⊘ ⋆ ⊗ ⊗ ⊗ 13.0

Tractor House ⊗ ⊗ ⋆ ⊗ ⋆ ⊗ ⊗ ⊘ ⊙ ⋆ ⋆ ⊙ ⊙ ⋆ ⊗ 35.0

Tractor Zoom ⊗ ⊗ ⊙ ⊗ ⋆ ⊗ ⊗ ⊗ ⊗ ⊙ ⋆ ⋆ ⋆ ⋆ ⋆ 32.0

Legend: ⊗: Not Implemented | ⊘: Partially Implemented | ⊙: Implemented | ⋆: Fully Implemented

0 10 20 30 40

50 60

70 80 90 100

Machine rental

Service rental

Filter search interface

Internal platform chat

Filtering by technical speciﬁcations

History and other reports

Feedback system in ads

28.6%

7.1%

57.1%

50%

7.1%

28.6%

21.4%

7.1%

14.3%

71.4%

85.7%

100%

14.3%

100%

85.7%

Not Implemented Partially Implemented Implemented Full Implemented

Figure 5: Implementation of the main criteria.

5.4 Discussion on the Platforms

In general, platforms such as AgriAffaires and Agrofy

stood out for implementing most of the criteria ade-

quately, although there is still room for improvement,

especially in usability. Features like geographic ﬁl-

ters, integration with interactive maps, and detailed

ad rating systems are limited, and if improved, they

could optimize the user experience and the efﬁciency

in searching and comparing products. Thus, in re-

sponse to the research questions of the study, we can

highlight the following points (Figures 6 and 7).

6 THREATS TO VALIDITY

This section aims to present the threats to the valid-

ity of the study and the procedures used to mitigate

them, following the postulates of Wohlin (2012) and

Verdacchia et al. (2023): Construct Validity: One

identiﬁed threat is the risk of not capturing all rele-

RQ1. What are the existing platforms

for agricultural machinery listings?

The study identiﬁed fourteen (14) platforms

focused on the rental, buying/selling, and

auctioning of agricultural machinery, including

Agrofy, Agriaffaires, and Tractor House. These

platforms have distinct business models and

varying levels of functionality implementation.

Figure 6: Answer to RQ1.

vant platforms in the search mechanism. Since the

study focused on gray literature, there is a possibility

that some tools were excluded due to not being visible

in the channels investigated. To mitigate this threat,

we developed a search string to be as comprehensive

as possible within its limitations.

Internal Validity: (i) The perceived threat is re-

lated to the possibility of biased evaluation criteria.

Since the group’s future work is likely to involve de-

veloping a system similar to the ones analyzed, there

Bridging the Gap in Agricultural Sharing Economy: A Systematic Review for Evaluating Information Systems for Machinery Efﬁciency

325

RQ2. What are the features of the

found platforms?

The most implemented features were the search

interface with ﬁlters and ﬁltering based on tech-

nical speciﬁcations. However, functionalities

such as service rental, rental history, internal

chat, and ad evaluation systems were found to

be insufﬁciently explored or absent among the

evaluated platforms.

Figure 7: Answer to RQ2.

may have been some subjectivity in the development

of the criteria. To mitigate this threat, the Plan-

ning Poker method was used during the assignment

of weights to help the reduction of individual bias;

(ii) Another threat relates to the possibility that crite-

ria may have been evaluated incorrectly in some plat-

forms. Considering the possibility that we may have

overlooked some functionality due to usability issues

or been excessively strict or lenient in certain evalua-

tions. To mitigate this threat, we conducted four eval-

uations: three individual and one joint. In the joint

evaluation, all criteria were thoroughly reviewed and

validated, ensuring greater accuracy and consistency.

Conclusion Validity: The main threat to our

study refers to the possibility of biased data analy-

sis, where the evaluation may vary depending on the

platform. To reduce this, we structured the analysis

by considering general points of inﬂection, ensuring

all platforms faced equal criteria.

7 MAIN CONTRIBUTIONS

The study presents several signiﬁcant contributions,

both to the agricultural sector and to the ﬁeld of IS.

Identiﬁcation of Gaps in Existing Systems: The

study identiﬁed that many platforms do not fully meet

the needs of users. For IS, this emphasizes the need

for user-centered designs (people) and more robust re-

quirements models for speciﬁc demands.

Application of the Diffusion of Innovations

Theory: The use of the Diffusion of Innovations The-

ory helped to understand the challenges of adopting

platforms in the agricultural sector. The theory pro-

vides a framework for analyzing the adoption of tech-

nologies and surpass cultural and economic barriers.

Proposal of Criteria for New Solutions: Cri-

teria were suggested for developing platforms, in-

cluding features such as advanced search, interactive

maps, and evaluation systems. This provides practical

guidelines for requirements engineering in IS, creat-

ing systems more aligned.

Exploration of Grey Literature as a Data

Source: The use of grey literature allowed for the

identiﬁcation of tools that are underdocumented in

academic sources, broadening the view of the agri-

cultural platform market. This approach comple-

ments traditional literature reviews in IS by including

emerging data and practical trends.

8 RESEARCH PERSPECTIVES

The results of this study reveal a series of gaps and op-

portunities that can guide future research in the shar-

ing economy for the agricultural sector.

Development of Essential Features: The analy-

sis showed low or absent implementation of key fea-

tures like service rental, chat systems, rental history,

and feedback mechanisms. Future research should

explore their development to enhance user experience

and platform efﬁciency.

Focus on Usability and Accessibility: Although

some platforms, such as Agriaffaires and Agrofy,

have stood out for implementing important criteria,

the overall usability of the tools remains a challenge.

Future studies could explore user-centered method-

ologies to assess and redesign interfaces, including

testing with real users in the agricultural sector, to en-

sure accessibility and ease of use, especially for farm-

ers with low technology familiarity.

Exploration of Hybrid Models: The analysis

identiﬁed that the evaluated platforms have different

business approaches (such as rental, buy/sell, and auc-

tion), but few integrate multiple models efﬁciently.

Future research could explore the development of hy-

brid platforms that combine rental, buy/sell, and ser-

vice functionalities, maximizing the reach.

Agricultural Machinery and Equipment API

Service: An emerging opportunity identiﬁed from

the analysis of this study is the creation of an API

(Application Programming Interface) service focused

on agricultural machinery and equipment. The API

service could centralize information on rental, buy-

ing/selling, location, and technical speciﬁcations of

agricultural equipment, making this information ac-

cessible to developers and users of other platforms.

The API could also include relevant data such as av-

erage prices, regional availability, and technical char-

acteristics of the equipment.

9 FINAL REMARKS

In this study, we conducted a qualitative analysis of

platforms focused on the rental and sale of agricul-

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tural machinery. However, the results revealed that,

although some platforms have advanced in the imple-

mentation of essential functionalities, there are still

signiﬁcant gaps, especially in terms of usability and

critical features.

The use of grey literature as a methodological ba-

sis proved to be an effective strategy for identifying

relevant tools, but it also revealed limitations in ac-

cessing complete and detailed information about the

existing platforms. Future studies could expand this

approach to include complementary data collection

methods, such as user interviews and in-depth anal-

ysis of successful cases.

Applying Diffusion of Innovations Theory clari-

ﬁed adoption challenges for the platforms. Compati-

bility, complexity, and observability emerged as key

factors. Many platforms struggled, especially with

agricultural compatibility, underscoring the need for

better adoption strategies.

As noted in the abstract, the work highlights col-

laborative efforts linking equipment owners to small

and medium producers. These initiatives can reshape

technology access, fostering inclusion, sustainability,

and competitiveness. Future advancements should

address gaps identiﬁed, offering tools to meet evolv-

ing market demands.

DATA AVAILABILITY

We are committed to promoting transparency and re-

producibility in research. In line with this commit-

ment, we provide all the data supporting the ﬁndings

of our study, which is openly available on Zenodo at

https://doi.org/10.5281/zenodo.14176810.

ACKNOWLEDGEMENTS

We are deeply appreciative of the resources, guidance,

and support provided by our laboratory and univer-

sity, which have been crucial in the development and

execution of this project.

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