Challenges of implementing AI marketing in
e-commerce businesses in North Macedonia
Gjorgjina Sherovska*
Abstract: This study investigates the challenges faced by e-commerce

*Ph.D., Expert in Marketing
Management and PR
Skopje, North Macedonia
gsherovska@gmail.com

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DOI 10.32015/JIBM.2024.16.2.7

Mednarodno inovativno
poslovanje =
Journal of Innovative Business
and Management

ISSN 1855-6175

businesses in North Macedonia regarding the implementation of
artificial intelligence (AI) marketing strategies. Engaging 115 active
participants in the sector, the research identifies barriers that hinder
the effective integration of AI marketing. Key areas of focus include
technological integration, resource limitations, and the relationship
between consumer expectations and AI-driven marketing initiatives.
The findings reveal a significant level of satisfaction with AI marketing
implementation, reflecting the resilience and adaptability of
businesses in navigating these challenges. The analysis highlights the
relationship between various factors—such as the level of AI
implementation, AI readiness, digitalization level, marketing activities
handling, and approaches to addressing challenges—on overall
satisfaction. Insights from this study aim to guide e-commerce
businesses in North Macedonia and similar contexts in successfully
adopting AI in their marketing strategies. Furthermore, the research
explores organizational readiness for AI marketing, emphasizing a
proactive approach that contributes to the evolving integration of AI
technologies in e-commerce practices.
Keywords: artificial intelligence, e-commerce, challenges,
marketing strategies
JEL classification: M10; M31; L81; C88; D83

Izzivi pri uvajanju trženja z umetno inteligenco
v podjetjih za elektronsko poslovanje v
Severni Makedoniji
Povzetek: Študija raziskuje izzive, s katerimi se soočajo podjetja za
elektronsko trgovanje v Severni Makedoniji pri uvajanju strategij
trženja z umetno inteligenco (UI). V raziskavi je sodelovalo 115
aktivnih udeležencev iz tega sektorja, pri čemer so bile identificirane
ovire, ki otežujejo učinkovito integracijo trženja z UI. Ključna področja
raziskave vključujejo tehnološko integracijo, omejitve virov ter odnos
med pričakovanji potrošnikov in trženjskimi pobudami, ki temeljijo na
UI. Rezultati kažejo na visoko stopnjo zadovoljstva z implementacijo
trženja z UI, kar odraža odpornost in prilagodljivost podjetij pri
premagovanju teh izzivov. Analiza izpostavlja povezavo med različnimi
dejavniki – kot so raven uporabe UI, pripravljenost za UI, stopnja
digitalizacije, način izvajanja marketinških aktivnosti ter pristopi k
reševanju izzivov – in splošnim zadovoljstvom. Ugotovitve študije so
namenjene usmerjanju podjetij za elektronsko trgovanje v Severni
Makedoniji in podobnih okoljih pri uspešnem uvajanju UI v trženjske
strategije. Poleg tega raziskava preučuje pripravljenost organizacij na
trženje z UI in poudarja proaktiven pristop, ki prispeva k razvijajoči se
integraciji tehnologij UI v prakse elektronskega poslovanja.
Ključne besede: umetna inteligenca, elektronsko poslovanje, izzivi,
marketinške strategije

1

Introduction
Artificial Intelligence (AI) is a domain of computer science that deals with the development
of intelligent computer systems capable of perceiving, analyzing, and reacting to inputs,
explains Spector (2006). As a critical tool for marketers, AI eliminates human faults such as
delays, biased approaches, and other minor flaws (Katyal, 2019). Marketers are familiar with
terms like artificial intelligence, artificial neural networks, pattern recognition, big data,
and automation. With these advancements, achieving high marketing results has become
more attainable within companies. Salvador (2013) defines online commerce, or ecommerce, as commercial transactions conducted via electronic devices, involving the
exchange of goods, services, or information. Expanding on this, Albertin (1999) describes ecommerce as encompassing the entire value chain of business processes within an electronic
environment, driven by the extensive use of communication and information technology to
achieve business objectives. Artificial intelligence is used by e-commerce companies, blogs,
and several online social and media websites to analyze our online activity and recommend
products and content to increase conversions and time spent on their sites (Bala and Verma,
2018).
In this paper, we explore the key challenges and opportunities for e-commerce businesses
in North Macedonia in adopting artificial intelligence marketing strategies. Specifically, we
investigate how factors such as AI implementation level, digital readiness, and organizational
support influence satisfaction with AI integration and overall business performance. A
globally customer-centric approach focused on customer needs plays a pivotal role in
organizational growth, as Vetterli et al. (2016) stress. Research on digital and technological
evolution in marketing has been considerably fast-paced, with Crittenden et al. (2019)
emphasizing that researchers are exploring the influence of technological advancements on
organizations' ability to manage customer needs and deliver offerings, as Kumar et al.
(2019a) agree. However, only in recent years has research at the intersection of artificial
intelligence (AI) and marketing gained more attention, with calls for further exploration of
AI-related topics and their roles in marketing, as noted by Davenport et al. (2020) and Kumar
et al. (2019b). AI is an emerging technology that helps organizations track real-time data to
analyze and respond swiftly to customer requirements (Wirth, 2018). E-commerce represents
the practice of buying and selling goods and services entirely online, serving various markets
through a wide range of smart devices, including tablets and smartphones. As Bloomenthal
(2022) explains, the e-commerce market is fiercely competitive, with everything from books
to clothes and financial services available online. Numerous enterprises are integrating AI
and machine learning into their operational processes. According to Russell and Norvig
(2016), AI describes machines (computers) that simulate cognitive and affective functions of
the human mind. This technology encompasses perception, reasoning, and actuation,
combined through algorithms that enhance customer service and performance, as Belanche
et al. (2020) explain. Paters et al. (2021) suggest that technologies collectively known as AI
can perform tasks that require human intelligence.
The significance of AI in transforming the e-commerce landscape
The marketing industry can greatly benefit from artificial intelligence (AI). It supports the
expansion of information and data sources, the development of complex and sophisticated
algorithms, and the enhancement of software's data management skills. Brands and
customers are interacting differently as a result of AI. The way this technology is used is
very much influenced by the sort of business and website, states Wang et al. (2023). The
field of artificial intelligence (AI) trains computers to understand and mimic human behavior
and communication, states Zhang (2021). Implementers of AI-based marketing solutions have
noted improvements in business model decisions, (Valter et al. 2018), new product
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development (Chan et al. 2011), communication (Paschen, 2019), pricing (Calvano et al.
2019), sales management (Flaherty et al. 2018), advertising (Kietzmann et al. 2018), and
personalized mobile marketing strategies (Tong et al. 2020). Experts have predicted the
substantial effect of AI in three industries, i.e., retail, education, and health care, as per
Ostrom et al. (2018). The retail industry with a high proportion of human work and
concurrent low-profit margins is a natural fit for AI applications, especially e-commerce
(Weberand Schütte, 2019). “Service AI," and if defined, means “configuration of technology
to provide value in the internal and external service environments through flexible
adaptation enabled by sensing, learning, decision-making, and actions,” according to Bock
et al. (2020). Therefore, service AI is not just about applying pre-programmed decisions; it
has learning ability as well, points Makridakis (2017). AI treats human images like cookies,
allowing for more personalized services based on customers' preferences. Some businesses
are experimenting with facial recognition to diagnose their customers' moods and, as a
result, make appropriate product recommendations, (Yang, 2021). AI can also be used for
trend and product forecasting. By analyzing historical data on past trends and consumer
behavior, AI algorithms can detect emerging trends and anticipate which products are likely
to be successful in the future. The technology can then make predictions on what products
are likely to be popular in the future based on this data. This can help eCommerce businesses
make more informed decisions when it comes to product development, marketing, and other
aspects of their operations (Niu, 2021). The concept of marketing has arrived at an
evolutionary point where adaptation to technology is imperative and the impact of AI under
each marketing mix component is obvious, i.e., for the product (hyper-personalization, new
product development, automatic recommendations, etc.), price (price management,
personalized dynamic pricing), place (convenience, speed, simple sales process, 24/7
chatbot support, etc.), and promotion (personalized communication, unique user
experience, creating wow factor, minimizing disappointment, etc.), explain Jarek et al.
(2019) and Dumitriu and Popescu (2020). The utilization of AI further provides a
comprehensive understanding of customer decision-making processes. AI algorithms are
capable of categorizing customers based on the data gathered, including the probability of
their returning to place additional orders. These insights can be leveraged to optimize
marketing efforts and effectively target the appropriate users at the opportune time.
Additionally, AI has the potential to provide managers with pertinent information regarding
customers who are at risk of departing by way of its ability to calculate the probable
customer lifetime value. This knowledge can be utilized to engage with customers and
persuade them to maintain a relationship with the company, in turn resulting in an increased
customer lifetime value and heightened online sales, (Danielkievich, 2022).
The potential impact of AI marketing on e-commerce in North Macedonia
Technologies help increase the efficiency, quality, and cost-effectiveness of services
provided by businesses, states Khrais L. (2020). The benefits of e-commerce as good
opportunities for developing enterprises, attracting new customers and expanding markets,
and improving financial performance have become even more evident during the
extraordinary crisis conditions. In specific periods, when many retail stores were closed, ecommerce became the only source and supplier of necessary goods for a large part of the
population and, simultaneously, an acceptable way of commercial business functioning,
agree Tran (2021) and Amsari et al. (2022).
North Macedonia is no exception to e-commerce's growth, which the COVID-19 pandemic has
accelerated. According to the Association for E-Annual Commerce's Report for North
Macedonia for 2020, the value of online transactions increased by 56% in 2020 compared to
2017. According to the same report, the number of internet selling points (or "e-selling
points") in North Macedonia expanded to 1,342 in September 2020, up from only 342 in 2018,
as stated in the report by the Ministry of Finance (2020). With an expected increase of 4.3%
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in 2023, the Macedonian eCommerce market contributed to the worldwide growth rate of
8.7% in 2023. Like in North Macedonia, global eCommerce sales are expected to increase
over the next few years. Seven markets are considered by ECDB within the Macedonian
eCommerce market. Hobby & Leisure is the largest market and accounts for 25.6% of the
Macedonian eCommerce revenue. It is followed by electronics with 21.3%, fashion with
17.8%, furniture & homeware with 11.6%, care products with 9.4%, DIY with 9.1%, and
groceries with the remaining 5.1%, as stated in the Ecommerce Market North Macedonia
Report (2023). The North Macedonia e-commerce market is witnessing a growth rate of 16%
over the forecast period from 2022 to 2027. North Macedonia's eCommerce market is
expanding, but it remains small regarding local businesses selling and consumers shopping
online. However, with regard to e-commerce, the National Bank reported a 61% increase in
Internet transactions by citizens in the first semester of 2021. With regard to statistical
performance and digital competitiveness, the collection of data based on the Digital
Economy and Society Index (DESI) indicators has been estimated to be available and fully
aligned in 87%, as stated in the Commission to the European Parliament report (2022).
Artificial intelligence manifests in diverse forms within the broader context of e-commerce,
encompassing both the global landscape and the specific domain of e-commerce in North
Macedonia. Artificial intelligence is able to predict how consumers will behave when
shopping, what products selected customers will choose, and how the company can build
and maintain the best possible relationships with them, (Soni, 2020). Furthermore, artificial
intelligence helps e-commerce follow business trends, changing customer needs in the
market. The company can gather a wide range of information, assess customers, and then
adequately respond to their requirements and habits by providing quality services. Once a
proper response is chosen, the business entity can expect increased customer comfort,
increased satisfaction, and a balance of supply and demand mechanisms, (Khrais L. 2020).
Artificial intelligence can be used not only to sell items but also to solve problems for clients.
Chatbots can now interpret the natural conversations of people and learn from these
interactions. Moreover, it is also possible to use chatbots in sales transactions. They can
make better connections with customers than even people who intake suggestions during
the sales process. For all businesses that are selling online in the global e-commerce
industry, having chatbot software is now almost a necessity, and many customers don't even
realize that they're talking with a computer, (Martinez, 2018). Artificial intelligence-based
applications can begin taking care of routine errands. They can alter promotion and deal
data for shoppers, states Jee (2016).
The author expresses as well that artificial intelligence algorithms can help retail and
different organizations better deal with their stock. It can robotize refilling demands and
advance supply chains. You can hand over your stock administration and inventory network
choices to machine learning-based applications. While e-commerce has become ubiquitous
in contemporary business landscapes, the academic discourse on this pervasive phenomenon
is notably limited. Despite its widespread adoption as a fundamental component of modern
commerce, there remains a conspicuous gap in the existing literature pertaining to
comprehensive analyses and in-depth examinations of the multifaceted dimensions of ecommerce. The paucity of scholarly works on this topic poses a significant challenge in
understanding the nuanced intricacies, emerging trends, and the evolving impact of ecommerce within various contextual frameworks. Consequently, there arises a compelling
need for extensive and systematic academic exploration to bridge this scholarly gap and
provide a more robust foundation for comprehending the complexities inherent in the ecommerce ecosystem.

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1

Methodology

1.1 Data Collection
The data collection process for this study took place in March 2024. A specialized invitation
was sent to e-commerce businesses in the North Republic of Macedonia, inviting enterprises
registered in the region to participate in an online survey. The survey consisted of a
structured questionnaire with twenty-five questions, estimated to take 5-7 minutes to
complete. The questionnaire was divided into four major sections: the first two gathered
basic information about the businesses and their AI implementation status, the third
addressed AI marketing challenges, and the final section explored AI attitudes in ecommerce. In addition to the survey, a multiple linear regression was conducted using
Minitab software. The dependent variable was satisfaction with AI marketing
implementation, while the independent variables included the level of AI implementation,
AI readiness, digitalization level, marketing activities handling, and the approach to
addressing challenges. This regression analysis provided deeper insights into how these
factors influence satisfaction with AI marketing.
1.2 Empirical Results
A total of 115 valid responses were collected from a successfully conducted survey
comprising 25 questions. The main purpose of this survey was to thoroughly examine the
distinctive challenges confronted by e-commerce enterprises in North Macedonia when
embracing AI marketing. The focus was on illuminating complexities related to technological
integration, resource constraints, and the dynamic interplay between consumer
expectations and AI-driven marketing initiatives. The results of this survey, in conjunction
with the theoretical framework, provide a foundation for developing ideas and concepts.

2

Discussion of results

The study received a total of 115 responses, out of which the nature of their e-commerce
business and the following are the results: 46.09% are in B2C (business-to-consumer), 32.17%
are in B2B (business-to-business), and 21.74% are in C2C (consumer-to-consumer). Each of
the respondents was asked regarding their current role in the e-commerce company, and
the results show that the majority of them are Chief Operations Officers (COO), which
represents 33.04% of the total. Second on the rankings are e-commerce managers with
24.35%. Third on the rankings are Chief Marketing Officers (CMOs) with 20%. Ranked fourth
in the survey are digital marketing managers, comprising 13.04% of the respondents.
Following closely, CEOs/founders hold the fifth position, representing 6.96%. Occupying the
last spot in the rankings are Chief Technology Officers (CTOs), with a modest percentage of
2.61%. In terms of business longevity, 49 out of the 115 respondents have been running their
e-commerce enterprises for 3-5 years, while 31 respondents have a business history of 5+
years. Additionally, 30 respondents have been in operation for 1-3 years, and a smaller group
of 5 respondents has been running their e-commerce business for less than 1 year.

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Figure 1: Q3 - What is the annual revenue range of your e-commerce business?

The results show that a significant portion of e-commerce businesses have an annual revenue
between approximately €4,036 and €16,186, making up a substantial segment of the
respondents. Following this, businesses with revenues ranging from approximately €16,186
to €80,932 make up the next largest group. A smaller but notable portion of businesses
reported annual revenues between approximately €809 and €4,036. A few businesses fall
into the higher revenue category, exceeding approximately €80,932 annually, while the
smallest group consists of businesses generating under approximately €809 per year. In terms
of workforce size, the majority of respondents reported having mid-sized teams of 11-50
employees, followed by those with slightly larger teams of 51-100 employees. Some
businesses operate with smaller teams of 1-10 employees, while a few reported having over
100 employees. A small fraction are solo entrepreneurs managing their businesses
independently. The primary sectors represented in the survey include fashion and apparel,
which dominates as the largest industry, followed by the beauty and personal care sector.
Other notable sectors include home and furniture, electronics, and automotive. When it
comes to target markets, more than half of the respondents focus on the national market.
A significant number cater to local markets, while a smaller portion targets international
customers, reflecting the diverse operational scope of these e-commerce businesses.

Figure 2: Q7 - How would you describe your level of digitalization in terms of business operations?

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Nearly half of the respondents believe their businesses are operating at a moderate level of
digitalization, while a significant portion consider themselves at a lower level. Only a smaller
fraction see their operations as highly digitalized. When it comes to online sales, over a third
of businesses generate a substantial portion of their revenue through digital channels, while
others report a more modest share. A small but notable group relies almost entirely on online
sales. Marketing activities in e-commerce businesses vary, with most respondents relying on
in-house teams. Others use a combination of in-house and outsourced teams, while some
depend entirely on external agencies or lack dedicated marketing support altogether.
Regarding AI adoption, more than half of the respondents are in the early stages of
implementing AI technologies, while others have made partial progress. A minority have fully
integrated AI, and a few have yet to begin. AI is most commonly applied in customer service,
with other uses including chatbots, inventory management, personalized recommendations,
and fraud detection. Challenges during AI implementation are common, with high costs being
the most frequently cited issue. Other challenges include a lack of skilled personnel,
difficulties with system integration, data privacy concerns, and resistance from employees.
Satisfaction with AI performance in e-commerce operations varies, with many respondents
expressing general satisfaction. However, a smaller number are either neutral or
dissatisfied. Most businesses allocate a small portion of their budget to AI initiatives, with
only a few committing more significant resources. Finally, the majority of respondents view
AI marketing as a very challenging aspect of their business. Others find it moderately or
somewhat challenging, while only a small fraction consider it not challenging at all.

Figure 3: Q16: What specific challenges do you face in implementing AI marketing strategies?

Respondents highlighted several key challenges in implementing AI marketing strategies. A
significant number expressed difficulty in understanding AI's full capabilities, while others
pointed to inadequate training for employees, which hinders effective use of AI tools. Many
also struggle with measuring the return on investment (ROI) from AI-driven marketing
efforts. Concerns over customer acceptance of AI-generated marketing strategies were also
noted, with a smaller group citing issues with integrating AI into their existing marketing
frameworks.
Regarding confidence in the accuracy and reliability of AI-generated insights for decisionmaking, a substantial portion of respondents expressed a high level of confidence, while
others felt generally confident in its effectiveness. Some were neutral, neither fully

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convinced nor dismissive of AI's accuracy, while a minority expressed doubts, showing varying
degrees of skepticism about its reliability.

Figure 4: Q19 - What measures have you taken to address the challenges associated with AI
marketing in your e-commerce business? (Select all that apply)

The last question in Section 3 focused on the measures respondents have implemented to
tackle the challenges associated with AI marketing in their e-commerce businesses. The
results reveal that a significant portion of participants have prioritized upgrading their AI
technology, while others have enhanced employee training programs. Additionally, some
have sought collaboration with AI experts or consultants to navigate these challenges
effectively. The next question assessed the perceived impact of AI marketing on the overall
performance of their e-commerce operations. A clear majority indicated that they
experienced a positive effect from AI marketing, while a smaller group viewed its impact as
neutral.
The final segment of the study aimed to explore the readiness of organizations to adopt AI
marketing strategies. The responses indicate a varied landscape of readiness among
participants. Many expressed confidence in their preparedness, while others remained
neutral, suggesting a cautious approach. A smaller number acknowledged they were not fully
ready, indicating a spectrum of readiness levels that reflects the diverse experiences of ecommerce businesses in integrating AI.

Figure 5: Q21. How would you rate your organization's readiness for adopting AI marketing
strategies?

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Respondents shared their insights on employee perceptions of AI marketing as a positive
addition to their roles. Many expressed a favorable view, with a significant portion feeling
positively about AI's impact, while a smaller group reported strong positivity. A notable
number remained neutral, and a few individuals had negative feelings toward its integration.
When asked how their organizations have fostered a culture that embraces AI marketing
technologies, responses varied. A considerable number of businesses implemented
awareness programs and training initiatives, while others encouraged cross-functional
collaboration to enhance understanding and acceptance. Some organizations recognized and
rewarded achievements in AI adoption, demonstrating their commitment to this transition.
A few opted to create specialized task forces focused on AI. In terms of effectively
communicating the benefits of AI marketing to customers, many respondents took a neutral
stance. Others found their organizations to be effective or very effective in these efforts,
although a small portion felt that their communication strategies fell short. Overall, these
insights reflect a complex landscape of attitudes and strategies surrounding AI marketing in
the workplace.

Figure 6: Q25. What role do you envision AI marketing playing in the future growth of your ecommerce business?
The final question in Section 4 is designed for short answers to collect unbiased opinions from
respondents. Among the responses, a notable portion believe that AI marketing will significantly drive
growth in their e-commerce business, while others view it as a moderate contributor. A smaller
percentage remain neutral, anticipate only a minor contribution, or assert that it will have no impact
on their business growth. To further explore these perspectives, a multiple linear regression analysis
was conducted using the statistical software Minitab® 19.1.1 (64-bit). The dependent variable,
representing satisfaction with AI marketing implementation, was derived from responses to specific
questions (Q13, Q17, Q19, Q21, and Q24) and was transformed using a Box-Cox transformation with
λ=0 to meet the necessary assumptions for analysis. Initially, the study examined nineteen
independent variables. However, through a process of backward elimination, which involved removing
variables that did not exhibit a statistically significant association (p-values greater than 0.05), five
independent variables remained for further analysis. This rigorous approach ensures that the findings
are both robust and meaningful, allowing for a clearer understanding of the factors influencing
satisfaction with AI marketing implementation.
The final result of the multiple linear regression is:

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The independent variables are Q10 (implementation level), Q20 (AI readiness), Q7
(digitalization level), Q9 (marketing activities handling), and Q18 (addressing challenges).
The analysis of the variance table is shown below where p-values of each term are less than
0.05 which proves our independent variables have statistically significant association with
the satisfaction with AI marketing implementation.
Source

DF

Adj SS

Adj MS

F-Value

P-Value

Regression

8

6.7243

0.84054

14.71

0.000

Q10

1

0.5693

0.56931

9.96

0.002

Q20

1

0.5339

0.53391

9.35

0.003

Q7

1

0.4441

0.44406

7.77

0.006

Q9

3

0.6564

0.21879

3.83

0.012

Q18

2

0.4934

0.24669

4.32

0.016

Error

106

6.0559

0.05713

Lack-of-Fit

55

3.7921

0.06895

1.55

0.057

Pure Error

51

2.2639

0.04439

Total

114

12.7802

Table 1: Analysis of Variance for Independent Variables Affecting Satisfaction with AI Marketing
Implementation
The coefficients table is shown below where the coefficients and P-values are shown.
Term
Constant
Q10
Q20
Q7
Q9
In-house marketing team
None
Outsourced marketing agency
Q18
Increased employee training
Invested in AI technology upgrades

Coef
0.819
-0.1260
0.1061
-0.1097

SE Coef
0.185
0.0399
0.0347
0.0394

T-Value
4.43
-3.16
3.06
-2.79

P-Value
0.000
0.002
0.003
0.006

VIF
1.74
1.41
1.39

-0.0820
0.250
-0.0426

0.0617
0.123
0.0741

-1.33
2.04
-0.57

0.187
0.044
0.567

1.88
1.96
1.82

-0.0672
-0.1802

0.0809
0.0782

-0.83
-2.31

0.408
0.023

3.11
3.07

Table 2: Coefficients and P-Values for Predictors of Satisfaction with AI Marketing Implementation
For the independent variable Q10 (implementation level) which is measured from 1 – not implemented
to 4 – fully implemented, the coefficient is negative which means as AI technologies are fully
implemented for e-commerce business, the satisfaction with AI marketing implementation goes down.

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This association is illustrated below using a scatterplot:

Figure 7: Scatterplot of Satisfaction vs Q10
For the independent variable Q20 (AI readiness) which is measured from 1 – very ready to 5 – not
ready at all, the coefficient is positive which means as organizations’ are not ready, the satisfaction
with AI marketing implementation goes up.
This association is illustrated below using a scatterplot:

Figure 8: Scatterplot of Satisfaction vs Q20
For the independent variable Q7 (digitalization level) which is measured from 1 – low to 3 – high, the
coefficient is negative which means as organizations’ digitalization level goes up, the satisfaction
with AI marketing implementation goes down.
This association is illustrated below using a scatterplot:

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Figure 9: Scatterplot of Satisfaction vs Q7
For the independent variable Q9 (marketing activities handling), the categories with coefficients
arranged in descending order are none, combination of in-house and outsourced, outsourced
marketing agency, and in-house marketing team. This means organizations with no marketing
team/agency is associated with more satisfaction with AI marketing implementation than with the
others. The descending order corresponds with lesser satisfaction as we go down the order.
For the independent variable Q18 (addressing challenges), the categories with coefficients arranged
in descending order are collaborated with AI experts or consultants, increased employee training, and
invested in AI technology upgrades. This means organizations with collaborations with AI experts or
consultants is associated with more satisfaction with AI marketing implementation than with the
others. The descending order corresponds with lesser satisfaction as we go down the order. The Pareto
chart is shown below in which the terms are arranged in descending order where terms on top have
more effect on the satisfaction with AI marketing implementation.

Figure 10: Pareto Chart of the Standardised Effects

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The multiple linear regressions adjusted R-square is 49.04% which means the model explains
49.04% of the satisfaction with AI marketing implementation variability.

3 Method

Figure 11: Normal Probability Plot

3

Conclusion

In conclusion, the study offers valuable insights into the e-commerce sector in the region,
revealing a diverse mix of businesses across B2C, B2B, and C2C models. Key decision-makers
such as COOs, e-commerce managers, and CMOs participated, reflecting a balanced
representation in the industry. Many businesses have been operating for several years,
indicating a degree of stability, while the fashion and beauty sectors emerged as primary
focus areas.

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The findings suggest that a significant number of respondents are in the early stages of AI
adoption, with applications ranging from customer service to inventory management.
However, challenges such as high costs, the need for skilled personnel, and data privacy
concerns were prevalent. Despite these obstacles, many respondents reported satisfaction
with the performance of AI technologies in their operations. While there is a general
confidence in AI-generated insights for marketing decisions, perceptions of the challenges
associated with AI marketing reflect a landscape of uncertainty. Organizations are taking
proactive steps to address these issues through investments in technology and employee
training. Overall, the study highlights a readiness among e-commerce businesses to embrace
AI marketing strategies, emphasizing the dynamic relationship between AI technologies and
e-commerce in North Macedonia.

4

Recommendations for further research

The current study provides a thorough understanding of the complex interconnections
between AI technologies and e-commerce in North Macedonia. However, there are several
areas that warrant further exploration in future research. Firstly, a detailed investigation
into the evolving challenges encountered during AI implementation could yield valuable
insights for developing more effective mitigation strategies. Additionally, a longitudinal
study tracking the progress of e-commerce businesses in response to technological
advancements and market shifts would offer a dynamic perspective. Moreover, future
research could delve into the nuanced impact of AI marketing strategies on specific industry
sectors within the region. A comparative analysis across varying business sizes and revenue
brackets may reveal differing patterns of AI adoption and performance. Understanding the
long-term sustainability and scalability of AI technologies in the context of North Macedonian
e-commerce businesses represents another fruitful area for exploration. Exploring the
interplay between AI technologies and emerging trends such as sustainability, ethical
considerations, and evolving consumer behaviors could contribute to a forward-looking
understanding of the challenges and opportunities within the e-commerce landscape. Lastly,
a cross-cultural examination, extending beyond North Macedonia, could offer a broader
perspective on the global implications of AI implementation in diverse e-commerce
ecosystems. These suggested directions aim to propel future research towards a more
nuanced understanding of the evolving relationship between AI technologies and ecommerce dynamics.

References
Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2020). Service robot implementation: a
theoretical framework and research agenda. The Service Industries Journal, 40(3-4), 203-225.
Bock, D. E., Wolter, J. S., & Ferrell, O. C. (2020). Artificial intelligence: disrupting what we know
about services. Journal of Services Marketing. https://doi.org/10.1108/JSM-01-2019-0047
Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S. (2019). Algorithmic pricing: What
implications for competition policy?. Review of Industrial Organization, 55(1), 155-171.
Chan, S. L., & Ip, W. H. (2011). A dynamic decision support system to predict the value of customer
for new product development. Decision Support Systems, 52(1), 178-188.
Commission to the European Parliament. (2022). Communication from the Commission to the
European Parliament, the Council, the European Economic and Social Committee, and the

14
Mednarodno inovativno poslovanje = Journal of Innovative Business and Management 2024 / Vol. 16 / No. 2

Committee of the Regions, North Macedonia Report 2022. Communication on EU Enlargement
Policy. https://www.pravda.gov.mk/Upload/Documents/North%20Macedonia%20Report%202022.pdf
Crittenden, W. F., Biel, I. K., & Lovely, W. A. (2019). Embracing digitalization: Student learning and
new technologies. Journal of Marketing Education, 41(1), 5-14.
Danielkievich, A. (2022). E-commerce artificial intelligence: 9 applications. Forbytes. Available at:
https://forbytes.com/blog/e-commerce-artificial-intelligence/#9-use-cases-of-ai-in-ecommerce
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change
the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42.
E-Commerce Market North Macedonia - Data & Trends. (2023). ECDB.com.
https://ecommercedb.com/markets/mk/all
Flaherty, K. E., Lassk, F., Lee, N., Marshall, G. W., Moncrief, W. C., Mulki, J. P., & Pullins, E. B.
(2018). Sales scholarship: Honoring the past and defining the future. Journal of Personal Selling &
Sales Management, 38(4), 413-421.
Jee, C. (2016). Uses of robotics in business: How will robots be used in future?
Kaur, N., Sahdev, S. L., Sharma, M., & Siddiqui, L. (2020). Banking 4.0: The influence of artificial
intelligence on the banking industry & how AI is changing the face of modern day banks.
International Journal of Management, 11.
Khrais, L. (2020). Role of artificial intelligence in shaping consumer demand in e-commerce. Future
Internet, 12(12).
Kietzmann, J., Paschen, J., & Treen, E. (2018). Artificial intelligence in advertising: How marketers
can leverage artificial intelligence along the consumer journey. Journal of Advertising Research,
58(3), 263-267.
Kumar, T., & Trakru, M. (2019). The colossal impact of artificial intelligence in e-commerce:
Statistics and facts. International Research Journal of Engineering and Technology, 6(5), 570-572.
Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial
intelligence in personalized engagement marketing. California Management Review, 61(4), 135-155.
Makridakis, S. (2017). The forthcoming artificial intelligence (AI) revolution: Its impact on society
and firms. Futures, 90, 46-60. https://doi.org/10.1016/j.futures.2017.03.006
Martinez, R. (2018). The power of artificial intelligence. Franchising World, 50(5), 92-94.
Ministry of Finance. (2020). PFM Monitoring Report North Macedonia January 2020. Available at:
http://finance.gov.mk/wp-content/uploads/2021/01/2020-PFM-Monitoring-Report-NorthMacedonia-EN-january-august-2020-FINAL-clean.pdf
Mordor Intelligence. (2023). North Macedonia e-commerce market size & share analysis - industry
research report - growth trends. North Macedonia E-commerce Market Size & Share Analysis Industry Research Report - Growth Trends.
Neerincx, M. A., & Schraagen, J. M. et al. (2021). Hybrid collective intelligence in a human–AI
society. AI & Society, 36, 217-238.
Niu, Y. (2021). Walmart sales forecasting using XGBoost algorithm and feature engineering. 2020
International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE),
Bangkok, Thailand, 2020, pp. 458-461. https://doi.org/10.1109/ICBASE51474.2020.00103.

15
Mednarodno inovativno poslovanje = Journal of Innovative Business and Management 2024 / Vol. 16 / No. 2

Ostrom, A. L., Fotheringham, D., & Bitner, M. J. (2018). Customer acceptance of AI in service
encounters: Understanding antecedents and consequences. In P. P. Maglio, J. C. Spohrer, & J.
Kieliszewski (Eds.), Handbook of Service Science. Springer Nature, Cham, pp. 77-103.
Paschen, U., Wilson, M., & Ferreira, J. J. (2020). Collaborative intelligence: How human and
artificial intelligence create value along the B2B sales funnel. Business Horizons, 63(3), 403-414.
Peeters, M. M. M., van Diggelen, J., & Van Den Bosch, K., Bronkhorst, A. (2021). Russell, P., &
Norvig, P. (2016). Artificial intelligence: A modern approach (3rd ed.). Pearson Education Limited.
Shabbir, J., & Anwer, T. (2018). Artificial intelligence and its role in the near future. arXiv preprint
arXiv:1804.01396.
Soni, V. D. (2020). Emerging roles of artificial intelligence in e-commerce. International Journal of
Trend in Scientific Research and Development.

16
Mednarodno inovativno poslovanje = Journal of Innovative Business and Management 2024 / Vol. 16 / No. 2