This research explores the gradual uptake of digital
technologies within Malaysia’s oil palm plantation sector,
despite the nation’s overall digital advancements. Focused on
Peninsular Malaysia, the study, utilising UTAUT2 and Lokman’s
Emotion and Importance Quadrant (LEIQ)™ frameworks, aims to
comprehend and identify the current state of digitalisation, as
well as the challenges hindering the acceptance and adoption of
digitalisation in the oil palm plantation sector. The focus of
the study is on agro-commodity companies operating in the oil
palm plantation sector to highlight the current state and the
challenges of acceptance and adoption for future technological
advancements in the oil palm industry. Despite the economic
significance of the sector, it faces hurdles in embracing
digital technologies, as this study revealed a moderate level of
digitalisation which is 72.26 per cent acceptance and adoption
rate. The study identifies challenges such as habituation to
technology, facilitating conditions, technologicalknowledge
gaps, price value, and worklife balance. Enhancing comprehension
of these challenges is imperative for industry stakeholders and
policymakers seeking to adeptly steer the course of digital
transformation. This understanding stands to enhance
productivity, efficiency, and sustainability within the domain
of the oil palm plantation sector. Future research should build
on these findings to formulate targeted policies and plans to
address challenges and align with user expectations to promote a
more technologically adaptive landscape in the oil palm sector.
Keywords: Digital Adoption, Oil Palm Plantation, Technology
Acceptance, UTAUT2, LEIQ™
02 introduction
Digitalisation, characterised by the integration of digital
technology into various facets of society, plays a pivotal role
in enhancing efficiency, effectiveness, and overall performance
across diverse sectors, including businesses and industries
(Mcfadden, 2022). The potential of digital technologies in
empowering decisionmaking processes is underscored by Jouanjean
(2019), who highlights their application in the agricultural
sector to bolster productivity, sustainability, and resilience.
This infusion of technology not only benefits farmers but also
opens avenues for efficiency and value creation within the
agricultural supply chains, influencing research, innovation,
and traceability (Jouanjean, 2019). In line with the global
trend towards digitalisation, recent times have seen the
Malaysian government’s launch of the Malaysia Digital
Initiative, aiming to cultivate emerging digital economy sectors
and replace the Multimedia Super Corridor agenda (Chandrasekaran
et al., 2022).
Despite the pervasive adoption of digital technology in
Malaysia, certain industries, particularly agriculture, have
lagged behind in this transition (Santiago, 2021). Agriculture,
a cornerstone of Malaysia’s GDP, is crucial for economic growth,
as emphasised in the National Key Economic Areas initiative.
However, its hesitance to embrace digital solutions hinders its
growth potential, creating an opportune moment for policymakers
to harness digital technologies for fortifying policy design,
implementation, and monitoring, as highlighted by Jouanjean
(2019). Within Malaysia’s agricultural landscape, the oil palm
plantation sector commands attention due to its robust research
capabilities and effective extension systems. This sector stands
as a prime exponent of Malaysia’s agricultural research prowess,
with significant contributions to agricultural trade exports,
particularly in oil palm and paddy production (International
Trade Administration, 2022).
The advancements of technologies in the digital economy,
particularly in agriculture, has the potential to significantly
transform farming practices by improving efficiency,
sustainability, and precision. Despite the potential benefits of
data-driven insights for precision, efficiency, and
sustainability in farming, the level and rate of digitalisation
in Malaysia’s oil palm plantation sector are lagging. While
Sarawak and Sabah boast the largest oil palm plantations
accounting for 28.6 per cent and 26.6 per cent of the total
Malaysian oil palm planted area, respectively, our research has
chosen to focus on Peninsular Malaysia, which comprises 44.8 per
cent of the total planted area (MPOB, 2022). The decision to
narrow our research to Peninsular Malaysia is driven by
practical considerations, including the limited six-month
timeframe for our study. Given the vastness of oil palm
cultivation in Sarawak and Sabah, covering all respondents in
these states within our time constraints would be unfeasible.
Therefore, this research concentrates on Peninsular Malaysia to
ensure a comprehensive and in-depth analysis within the
available time frame. This research aims to analyse the current
state of digitalisation in Peninsular Malaysia’s oil palm
plantation sectors and to identify challenges to acceptance and
adoption.
The research aims to analyse the current state of digitalisation
in Peninsular Malaysia’s oil palm plantation sector and identify
challenges to acceptance and adoption. Henceforth, the research
sets its objective to measure the rate and the level of
digitalisation acceptance and adoption within the oil palm
plantation sector in Peninsular Malaysia using the UTAUT2 model;
and to identify the issues and challenges contributing to
digitalisation acceptance and adoption using LEIQ™.
03 literature review
Digital Divide and Digital Technology Adoption
The digital divide refers to discrepancies in ICT (Information
and Communications Technology) access, utilisation, and
outcomes. Customers or clients may be unable to use technology
or AI systems because they lack access to the most recent
personal technology (such as smartphones, tablets, etc.),
Internet connectivity, or ICT skills (Ghandour, 2021). In
contrast, according to Charness and Boot (2022), systems that
use adaptive technology to mix extended reality with AR
intelligence provide exciting new approaches to overcoming the
digital divide imposed by an individual’s age.
Education
emerges as a major contributor to the digital divide, as
highlighted by Lythreatis and colleagues (2022). While
addressing the digital gap is crucial, particularly in the
context of agricultural production, rural communities can
benefit from the ongoing adoption and accessibility of digital
technologies, irrespective of their direct connection to
agriculture. Encouraging farmers and professionals in this
sector to embrace modern technologies is crucial for enhancing
the competitiveness of the agricultural sector. The integration
of digital technology in agriculture holds the potential for
various benefits, including cost reduction and improved product
quality (Bolfe et al., 2020).
However, challenges may
arise in the digitisation process due to the digital divide.
Insufficient access to technology infrastructure and low levels
of digital literacy in rural areas may hinder the adoption of
digital technologies. While digital technologies improve
production in plantation industries, they have little to no
impact on the wellbeing and income of small-scale farmers in
rural areas (Rosnan & Yusof, 2023).
Technology Applications in Agriculture
Within the dynamic landscape of modern agriculture, technology
applications play an important role in transforming traditional
practice and enhancing crop production. This section explores
two (2) significant areas which are application of big data
analytics in agriculture and the list of smart tool technologies
available in the agriculture domain particularly in the oil palm
plantation sector.
Application of Big Data Analytics in Agriculture
The applications of big data analytics in agriculture are quite
diverse, but here are some of the most notable ones, such as
precision agriculture, crop monitoring, yield prediction,
disease detection, and supply chain optimisation (Coble et al.,
2018).
In precision agriculture, big data analytics can be used to
identify patterns and trends in soil moisture, temperature, and
other environmental factors, allowing farmers to optimise their
use of resources like water and fertiliser (Jatav et al., 2019,
Kaur et al., 2021).
Whereas in crop monitoring, the data from sensors and other
sources, farmers can be analysed to gain insights into the
health and growth of their crops, allowing them to take
corrective action when necessary. Concerning crop monitoring, it
can drive yield prediction, disease detection and supply chain
monitoring. Big data analytics can be used to model and predict
crop yields based on various factors, such as weather patterns,
soil quality, and historical yield data.
Other than that, by analysing data from sensors and other
sources, farmers can identify signs of disease or infestation in
their crops and take action before it spreads. Furthermore, by
analysing data on crop yields, weather patterns, and other
factors, agricultural companies can optimise their supply chains
to ensure that crops are delivered to market as efficiently and
cost-effectively as possible.
Overall, big data analytics has the potential to revolutionise
the way we approach agriculture, by enabling farmers and
agricultural companies to make more informed decisions based on
data-driven insights.
Smart Tools
Smart tools in agriculture are technological devices and
software applications that use data analytics, artificial
intelligence, and other advanced technologies to optimise
agricultural operations and increase productivity (Alreshidi,
2019; Mohamed et al., 2021; Saiz-Rubio & Rovira-Más, 2020).
These tools can help farmers and agricultural businesses make
better decisions about planting, harvesting, and managing crops.
Here are some examples of smart tools in agriculture (Liu et
al., 2018; Saiz-Rubio & Rovira-Más, 2020; Khan et al.,
2021):
Sensors
Sensors can be placed in the soil to monitor soil moisture,
temperature, and nutrient levels. This data can be used to
optimise irrigation and fertilisation practices and reduce
water waste.
Drones
Drones have the potential to revolutionise plantation
management by offering numerous benefits and applications.
Equipped with advanced sensors and imaging technologies,
drones provide real-time, highresolution data on crop
health, irrigation needs, pest infestations, and vegetation
monitoring. To comprehensively explore the potential of
drones, it is crucial to understand the industry’s
expectations regarding their usage and identify areas where
they seek to expand drone applications, along with the
associated challenges.
Precision farming software
Precision farming software uses data analytics and machine
learning algorithms to analyse data collected from sensors
and drones to create customised planting and harvesting
plans. This can help farmers increase yields, reduce waste,
and save time and resources.
Automated machinery
Automated machinery, such as robotic harvesters, can
increase efficiency and reduce labour costs. These machines
can be programmed to perform specific tasks, such as picking
and sorting crops.
Weather forecasting tools
Accurate weather forecasting can help farmers make informed
decisions about when to plant, irrigate, and harvest.
Smart weather forecasting tools
Provide real-time data and alerts about weather patterns and
potential risks.
Artifical Intelligence (AI)
AI technologies, including machine learning and data
analytics, are increasingly being integrated into
agricultural processes. AI can enable continuous data
collection and analysis, allowing for automation in various
aspects of farming. Machine learning algorithms can assist
in crop management, disease detection, and yield prediction,
ultimately enhancing decision-making and resource allocation
in agriculture.
By integrating AI into these smart tools, agriculture can
benefit from continuous data collection and machine learning,
enabling automation and more informed decision-making. AI’s
ability to process vast amounts of data and identify patterns is
poised to further enhance the efficiency and sustainability of
agricultural practices.
Foundation of Digitalisation in Agriculture
Over the past 26 years, Australian agriculture has demonstrated
a remarkable capacity for productivity growth, setting a
noteworthy standard for the industry (AgFunder, 2021). As part
of digital agriculture priority, towards achieving the vision of
Digital Economy Australian 2030, the Australian Government has
identified five (5) foundational focus areas.
The
first is leadership. To lead the transformation of the
agricultural sector, it is crucial to enhance connection and
coordination across the industry, encouraging the pooling of
resources and providing a clear plan to unify stakeholders
around collective long-term goals. Secondly is the skills. There
is a need to focus on delivering the necessary skills and
expertise required by both the current and future workforce to
modernise the sector effectively. Effective data and governance
practices, such as maximizing data use, ensuring good data
management, implementing common data standards, and promoting
interoperability, are essential for enhancing data and
governance within the agricultural domain. Fourthly,
opportunities and value propositions. Efforts should be directed
towards helping producers understand and realise the benefits of
digitising their businesses, ensuring appropriate and agile
regulation, while fostering faster commercialisation. Lastly, it
is imperative to assist agricultural businesses in understanding
their connectivity options and facilitating access to the
infrastructure they need for seamless integration into the
digital landscape (AgFunder, 2021).
04 methodology
The research model employed for this study was the extended
Unified Theory of Acceptance and Use of Technology (UTAUT2)
model, utilised to assess the acceptance and adoption of
Digitalisation Technology in the Malaysian Agriculture Sector.
The original UTAUT model, developed by Venkatesh et al. (2003),
aimed to explain and predict the acceptance of technology in an
organisational context. It stands out as one (1) of the most
comprehensive models in technology acceptance, integrating
components from eight (8) prominent models in information
technology research.
The UTAUT2 model, stemming from
the UTAUT constructs, posits that hedonic motivation (HM), price
value (PV), habit (HT), and time since the first use of the
technology (comprising a total of seven (7) independent
constructs or variables) collectively impact the intention to
use technology. Behavioural Intention (BI) serves as the
mediating variable, while Use Behaviour (USE) is the dependent
variable. According to Venkatesh, Thong, and Xu (2012), the
UTAUT2 model incorporates individual differences such as age,
gender, and experience as moderators, influencing the effects of
these constructs on BI and technology use. The UTAUT2 model is
particularly applicable in the introductory phase, encompassing
adoption and initial use, of the targeted technology. This study
fully embraced the UTAUT2 model, with Figure 1 illustrating the
UTAUT2 model.
Figure 1: UTAUT2 Model (Venkatesh et al., 2012)
While delving into the issues and challenges that impacted the
adoption of digitalisation, this study established the
groundwork for Lokman’s Emotion and Importance Quadrant (LEIQ™)
model. This model, rooted in Kansei Engineering, served as a
user-friendly tool for unveiling individuals’ implicit
experiences related to digitalisation issues. The LEIQ™ model
facilitated the identification of emotions and their influence
on decision-making, productivity, well-being, and overall
quality of life. It offered a structured framework for
categorising these experiences, the contributing factors, and
their significance in people’s interactions with specific
stimuli.
In the context of this research, the LEIQ™
model was applied to comprehend challenges, facilitators, and
future expectations directly from the primary sources—the
individuals engaged in utilising technology within the sector.
Utilising the emotion vs. importance axes, the model highlighted
the significance of identified factors in shaping people’s
implicit experiences. The quadrant, illustrated in Figure 2,
comprised four (4) spaces.
1. Positive
experience and important quadrant 2. Positive
experience and not important quadrant 3. Negative
experience and importance quadrant 4. Negative
experience and not importance quadrant
Figure 2: The LEIQ™ Model by Lokman (2018)
05 finding and analysis
Analysis and Discussion on Current Rate and Level of
Digitalisation Acceptance and Adoption within Oil Palm
Plantation
This section presents the results based on the analysis of the
data acquired through a quantitative survey questionnaire
developed based on UTAUT2 constructs. This section gives an
in-depth evaluation of the acceptance and adoption of
digitalisation within the oil palm plantation industries in
Malaysia. The study conducted a reliability test to recheck the
knowledge of the study among the first 30 respondents through
the questionnaire.
Table 1: Pilot Test Results
To check the reliability test, the values of Cronbach’s Alpha
are calculated and recorded in Table 1. Based on the presented
values for Cronbach’s Alpha in this table, it could be claimed
that the reliability of the research measurement tool is
statistically acceptable. Reliability is acceptable if
Cronbach’s Alpha equals 0.7 or more (Tavakol & Dennick,
2011). The scales show good reliability with Cronbach’s Alphas
> 0.7. The parameters were assessed how past interactions
with these tools shaped their inclination to seamlessly
incorporate them into their daily work routines. The structural
equation modelling was executed to measure the acceptance of
digitalisation/automation within the oil palm plantation sector
in Peninsular Malaysia. This was because it consisted of many
relationships from the parameters towards the dependent
variable, which was Use Behaviour (UB). Further tests on 130
respondents were conducted, and the results are shown in Figure
3 and Table 2 below.
Figure 3: The UTAUT2 Model of the Digitalisation Technology
Acceptance and Adoption within the Oil Palm Plantation Sector in
Peninsular Malaysia
The study validates all research hypotheses as evidenced by
consistently positive coefficient values, indicating a positive
impact on the indicator. Specifically, H1 is strongly supported
with a significant coefficient of 0.674, confirming that
performance positively influences the behavioural intention to
adopt and use technologies among individuals in oil palm
plantations. Additionally, effort expectancy, social influence,
price value, hedonic motivation, facilitating conditions, and
habit also exhibit positive impacts on individuals’ behavioural
intentions in this context.
Furthermore, the
established direct relationship affirms that behavioural
intention, facilitating conditions, and habit positively
influence the use behaviour of adopting and utilising
technologies among individuals in oil palm plantations. Overall,
the outcomes are robust with an impressive overall score of
0.59, signifying that 59 per cent of the total variation in
technology adoption and use among individuals in oil palm
plantations is explained by the specified independent
indicators, with the remaining percentage attributed to other
factors.
Table 2: Descriptive Analysis of Digitalisation Acceptance
within the Oil Palm Plantation Sector in Peninsular Malaysia
Table 2 shows the descriptive analysis of the surveys.
Performance Expectancy (PE) factor indicates the highest mean
score of 4.169 out of 5.00, followed by Hedonic motivation (HE)
with 4.128 out of 5.00, Social Influence (SI) with 3.946 out of
5.00, Behavioural Intention (BI) 3.903 out of 5.00, Effort
Expectancy (EE) 3.787 out of 5.00, Facilitating Conditions (FC)
3.715 out of 5.00, Use Behaviour (UB) 3.613 out of 5.00, Price
Value (PV) 3.603 out of 5.00, and Habit (HT) 3.554 out of 5.00.
According to the results, this indicates that the level of
adoption is mostly between agree and neutral in adopting and
using the technologies among individuals working in oil palm
plantations.
Figure 4: Rate and Level of Digitalisation within Peninsular
Malaysia's Oil Palm Plantation Sector
The current rate and level of digitalisation within Peninsular
Malaysia’s oil palm plantations that were measured by utilising
the UTAUT2 model are shown in Figure 4. Thus, the results show
moderate acceptance (72.26 per cent) for digitalisation within
oil palm plantations, with a level between agree and neutral.
Analysis and Discussion for Issues and Challenges using LEIQ™
This section analysed and discussed the issues and challenges
associated with the implementation of digitalisation within the
oil palm plantation industry. The challenges identified through
focus group discussions provided valuable insights into the
obstacles faced by stakeholders in adopting and integrating
technology into their daily work lives. These challenges were
categorised into several themes, including habit, facilitating
conditions, price value, technology literacy, technology
challenges, technology hazards, and work-life balance. Each
theme represented unique obstacles that needed to be addressed
to ensure the successful implementation of digitalisation.
Challenges in Digitalisation Implementation within the Oil Palm
Plantation Sector
After conducting a thorough analysis of the challenges
highlighted by the participants during the FGD session, it
became evident that three (3) primary themes posed significant
obstacles to the successful implementation of digitalisation
within the oil palm plantation sector. These themes were
habit, facilitating conditions, and
price value as depicted in Figure 5 above.
The
theme of habit refers to the ingrained behaviours and
routines that might inhibit the adoption and acceptance of
digital technologies within the sector. Participants expressed
concerns about resistance to change and the need to overcome
existing habits to fully embrace digitalisation.
Facilitating conditions
emerged as another prominent theme, underscoring the importance
of having the necessary infrastructure, resources, and support
systems in place to facilitate the implementation of digital
technologies. Participants emphasised the need for reliable
internet connectivity, adequate training, and technical support
to ensure smooth and effective digitalisation processes.
Price value
was identified as a significant factor influencing the adoption
of digitalisation within the oil palm plantation sector.
Participants expressed concerns about the cost-effectiveness and
return on investment of implementing digital technologies. They
emphasised the need for clear benefits and tangible outcomes to
justify the financial investment required.
In
addition to these main themes, further analysis of the
challenges raised by participants revealed additional themes
initially categorised as ‘Others’. Upon consolidation, these
themes were further categorised as
Technology Literacy and Work-Life Balance.
The theme of Technology Literacy encompassed
participants’ concerns regarding the level of knowledge and
skills required to effectively utilise digital technologies.
They emphasised the need for training and educational programmes
to enhance technology literacy among stakeholders.
Lastly,
Work-life Balance emerged as a crucial theme, reflecting
participants’ concerns about maintaining a healthy equilibrium
between work responsibilities and personal life in the context
of digitalisation. They stressed the importance of establishing
clear boundaries, promoting flexibility, and addressing the
potential negative impacts of digital technologies on work-life
balance.
06 recommendations
Considering the challenges outlined in this study, it is vital
for stakeholders in the oil palm industry to consider the
following key recommendations for advancing the implementation
of digital transformation.
Examining the challenges hindering the implementation of digital
transformation
Stakeholders in the oil palm industry can derive significant
benefits from a thorough analysis of the acceptance rate
obtained from the survey. By carefully scrutinising the
challenges that hinder the implementation of digital
transformation, the stakeholders and the policymakers can
acquire valuable insights to inform their decision-making
processes. They also can devise effective strategies to ensure
the successful implementation of digital technology in the oil
palm sector, thereby paving the way for future advancements and
growth.
Include a diverse range of locations and respondent sampling for
future research
Future research should explore the applicability of these
findings to different industries to enhance their broader
relevance. Subsequent research should involve a more extensive
and diverse sample to increase the representativeness of the
findings and ensure a more comprehensive understanding of
technology adoption challenges. Future studies should also
encompass a more diverse range of locations to examine potential
cross-cultural variations in technology adoption challenges and
recommendations.
Investigate the impact of technology adoption on the oil palm
industry
In terms of future work, further investigation is recommended to
explore the long-term impact of technology adoption on the oil
palm industry. This includes examining its effects on
productivity, sustainability, and socio-economic factors.
Understanding these long-term impacts will provide valuable
insights for industry stakeholders and policymakers.
Identifying specific strategies and interventions
Future research should also concentrate on identifying specific
strategies and interventions to address the challenges
identified in the study. For example, developing training
programmes to enhance technological literacy among industry
professionals and promoting a culture of innovation within the
industry can help overcome barriers to technology adoption.
Additionally,
studies can be suggested to examine the role of government
policies and regulations in facilitating technology adoption
within the oil palm industry. This includes exploring the
effectiveness of incentives for digitalisation and support for
infrastructure development to create an enabling environment for
technology adoption. Lastly, exploring the potential benefits of
emerging technologies, such as the Internet of Things (IoT) and
Artificial Intelligence (AI), in the oil palm industry is an
important area for future research. Investigating their
applications in precision farming, resource optimisation, and
supply chain management can uncover new opportunities for
enhancing industry practices.
07 conclusion
In exploring the acceptance and adoption of digitalisation
within Malaysia’s oil palm plantation sector, the study revealed
a significant technological gap, with an acceptance to the
adoption of digital solutions rate of 72.26 per cent. This gap
persists despite the transformative potential of digital
solutions to enhance efficiency, sustainability, and overall
performance in plantation practices. Focused on Peninsular
Malaysia due to practical constraints, the analysis highlighted
critical challenges, including habituation to technology,
insufficient facilitating conditions, knowledge gaps in
technology, financial concerns, and issues related to work-life
balance. These challenges impede the seamless integration of
digital solutions within the oil palm plantation sector.
By
employing models such as UTAUT2 and LEIQ™, the research
identified challenges positively influencing the adoption of
digitalisation. These insights are crucial for stakeholders,
enabling them to formulate strategies and make informed
decisions. Industry stakeholders could navigate the path to
digital transformation more effectively by understanding these
challenges, thereby improving productivity, efficiency, and
sustainability within the oil palm plantation sector. Overall,
the analysis of the challenges identified during the FGD session
provided valuable insights into the multifaceted nature of
digitalisation implementation within the oil palm plantation
sector. By addressing these themes and developing targeted
strategies, stakeholders could overcome the challenges and
maximise the benefits of digital transformation.
In
summary, recognising and addressing these challenges is crucial
for the sector’s digital transformation, impacting the level and
rate of digitalisation acceptance and adoption. This research
establishes a foundation for strategic enhancements in
Malaysia’s oil palm industry, fostering a more technologically
adoptive and adaptive landscape.
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