Indonesian Journal of Community Services, 4 .
, 53-60
INDONESIAN JOURNAL OF COMMUNITY SERVICES
Homepage: https://ojs.
org/index.
php/ijcs ISSN: 2829-7733 (Onlin.
Volume 4 Issue 1 May .
DOI: 10.
47540/ijcs.
Page: 53 Ae 60 Artificial Intelligence and Agritourism Development: Mixed Feelings on Digital and Social Media Marketing in Africa Rahabhi Mashapure1.
Julius Tapera2.
Purity Hamunakwadi3.
Admire Mthombeni4.
Bronson Mutanda4.
Lovemore Chikazhe1 Department of Entrepreneurship and Business Management.
Chinhoyi University of Technology.
Zimbabwe Assistant to the Vice-Chancellor.
Lupane State University.
Zimbabwe Department of Building and Human Settlements Development.
Nelson Mandela University.
South Africa Department of Business Management.
Manicaland State University of Applied Sciences.
Zimbabwe Corresponding Author: Rahabhi Mashapure.
Email: cmashapure29@gmail.
ARTICLE INFO
ABSTRACT
Keywords: Agritourism Development.
Artificial Intelligence.
Digital and Social Media Marketing.
Mixed Feelings.
The emergence of artificial intelligence and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals.
Artificial intelligence has been advancing rapidly in recent years, measured both in terms of the quantity of resources devoted to it and also in terms of its outputs.
Artificial intelligence is increasingly reshaping businesses by performing various tasks, constituting a major source of innovation, yet threatening human jobs.
The article reviews recent research in this area that suggests that AI and robotics have the potential to increase productivity and growth of agritourism, but may have mixed effects on labor, particularly in the short run.
Using the positivism research philosophy, the study also sought to examine the insights, attitudes, and involvements of participants toward AI-driven marketing technologies in agritourism.
Further, it assessed the socio-cultural, economic, and environmental influences of digital and social media marketing on African agritourism It also sought to ascertain paramount practices, challenges, and opportunities for leveraging AI technologies to stimulate sustainable agritourism development in Africa.
The study considered current and potential policies around AI that could potentially help boost agritourism development while also mitigating any labor market downsides, including evaluating the pros and cons of AI on African agritourism development.
The study finds that organizational factors, positive socio-cultural factors, economic factors, and environmental factors play a crucial role in the adoption of artificial intelligence by agritourism industries.
Based on the study findings, the paper recommended that the fast adoption of AI needs to be supported by the necessary regulatory insight and oversight for AI-based technologies to enable agritourism sustainable development.
Failure to do so could result in gaps in transparency, safety, and ethical standards.
The findings also informed recommendations for further study and guided the discourse on implications for policy and practice, which other researchers, policymakers, and practitioners could potentially draw learning points from.
Received Revised Accepted : 29 June 2024 : 19 May 2025 : 28 May 2025 personalized content .
udience analysi.
and how the images they share enable savvy marketers to recognize the logos of brands or companies active in social media content .
mage analysi.
Artificial intelligence (AI) tools provide effective support to social media marketers in their tasks to optimize audience, image, and sentiment analyses by identifying the branded content that carries out high
INTRODUCTION
Artificial intelligence (AI) technologies are greatly effective in monitoring social media to get a comprehensive knowledge of what people interacting on social networks are discussing about a brand in their posts and comments .
entiment analysi.
(Bobitan et al.
, 2.
They are useful as well to determine how they can be approached with Indonesian Journal of Community Services, 4 .
, 53-60 customer engagement with social media (Dwivedi et al.
, 2.
Agriculture and tourism are two vital sectors in many African economies.
Agritourism, which involves activities that incorporate tourism with agricultural practices, has been growing in popularity as it offers prospects for income generation and rural development.
According to (Karthik & Gajanand, 2.
, agritourism allows a tourist to visit an agricultural setting or a working farm, whereas (Ait-Yahia Ghidouche et al.
, 2.
are of the opinion that agritourism is used by farmers as a strategy to contribute and enhance agricultural development on their farm.
in some cases, they even use it as a way to stimulate sustainable rural development.
Some of the first forms of agritourism in the world were found in the United States of America (US) and Italy.
Agritourism gained popularity in the 1920s in the US, when travel became more common .
an Zyl & van der Merwe, 2.
In recent years, the convergence of artificial intelligence (AI), digital media, and agritourism began as a dynamic field with vast potential for economic growth and sustainable development, particularly in the African context.
Agritourism, which involves the assimilation of agricultural activities with tourism proficiencies, offers a unique opportunity to showcase Africa's rich cultural heritage, diverse landscapes, and traditional farming practices to domestic and international visitors.
Africa, endowed with rich natural resources and cultural diversity, is increasingly looking towards agritourism as a means to diversify its economy, alleviate poverty in rural areas, and encourage sustainable agriculture practices.
However, the effective promotion and marketing of agritourism experiences pose significant challenges on a continent characterized by digital gaps, infrastructural limitations, and socio-economic The advent of digital and social media platforms has transformed the way industries engage with customers, market their products, and build brand awareness.
In the context of agritourism, digital and social media marketing offers unparalleled opportunities to reach a global audience, showcase unique experiences, and facilitate an uninterrupted interface between farmers, tourists, and other stakeholders.
Customers continuously network with social media sites such as Facebook.
Twitter.
LinkedIn.
Pinterest.
Instagram.
TripAdvisor.
WhatsApp.
TikTok.
Weibo.
YouTube and Chatbots.
Social media is one of the key sectors where the agritourism industry can both skyrocket performance and efficiency by using artificial intelligence.
With the help of artificial intelligence (AI), data about agritourism activities on social media is constantly being compiled and analyzed.
Social media is presently being used to infer social behavior and derive tendencies in combination with big-data analysis tools (Wang et al.
, 2.
For example, social media users can share their travel experiences on TripAdvisor or Booking.
In addition, social media customers .
, influencer.
can serve as endorsers to promote travel destinations on social sites (Shan et al.
, 2.
However, the adoption of digital and social media marketing strategies in African agritourism is met with mixed feelings and presents a complex set of opportunities and While these platforms have the potential to amplify the visibility of agritourism destinations, empower local communities, and foster sustainable development, they also raise concerns related to cultural commodification, digital exclusion, and environmental sustainability.
The literature indicates that social media marketing in general has gained significant momentum across all sectors, with micro-hospitality establishments having fully embraced it in more economically developed countries (Choo & Park, 2022.
Nkosana & Skinner.
Sharma et al.
, 2.
Despite the rising interest in the intersection of artificial intelligence (AI), digital media, and agritourism in Africa, there remains a scarcity of empirical investigation examining the effects, obstructions, and prospects associated with the adoption of digital and social media marketing strategies in this context.
Zimbabwe, like many other African nations, faces unique challenges and opportunities when it comes to leveraging artificial intelligence artificial intelligence (AI) and digital/ social media marketing for agritourism Agritourism in Zimbabwe presents a promising avenue for economic development, rural revitalization, and cultural preservation.
However, the integration of artificial intelligence artificial intelligence (AI) and digital/social media marketing into agritourism is fraught with challenges and mixed perceptions among stakeholders.
The digital Indonesian Journal of Community Services, 4 .
, 53-60 divide, technological barriers, economic constraints, perceptual challenges, inconsistent policies, and a lack of government support for digital infrastructure development and artificial intelligence (AI) innovation further complicate the landscape (Dwivedi et al.
, 2.
There is a need for coherent strategies and policies that encourage the adoption of these technologies while addressing the concerns of various stakeholders.
Despite the potential benefits of these technologies in enhancing the efficiency of agricultural practices, improving visitor experiences, and expanding market reach, several issues hinder their adoption and effective It is from this background that this paper seeks to explore the role of artificial intelligence in shaping digital and social media marketing practices in the context of agritourism development in Africa.
By adopting a positivism approach, this paper aims to investigate the current landscape of digital and social media marketing strategies employed in African agritourism operators.
The study examines the insights, attitudes, and involvements of participants towards artificial intelligence (AI)driven marketing technologies in agritourism.
further assesses the socio-cultural, economic, and environmental influences of digital and social media marketing on African agritourism destinations.
The study also ascertains paramount practices, challenges, and opportunities for leveraging artificial intelligence (AI) technologies to stimulate sustainable agritourism development in Africa.
Through these objectives, this paper endeavors to contribute to the theoretical understanding of the complex interplay between artificial intelligence (AI), digital media, and agritourism in Africa while also providing practical insights and policy commendations for interested parties involved in the development and management of agritourism outcome in a sample of the population at a certain moment in time (Wang et al.
, 2.
Respondents in the study were agritourism owners in Zimbabwe.
The population comprised male and female participants in the agritourism business.
The total number of population was not known due to the fact that most agritourism businesses operating in Zimbabwe are not registered and they operate Therefore, 146 agritourism businesses were used.
The sample was chosen as it is within the range of similar studies conducted by (Bhatta & Ohe, 2.
, (Choo & Park, 2.
, (Tew & Barbieri, 2.
who used sample sizes of 85, 164, and 196 Convenience sampling was used to select respondents since some of the agritourism businesses were scattered and not reachable by the time of the study.
Primary data were gathered using a standardized questionnaire with a five-point Likert The responders used a Likert scale with 1 being strongly disagree and 5 being strongly agree.
The Likert scale ranged from 1 strongly disagree to 5 strongly agree and the respondents were asked to show their response by the use of a tick or a visible The instrument design components were adapted to fit the current studies from related prior
RESULTS AND DISCUSSION
Respondents Rate Agritourism businesses were the targeted respondents during data collection with regard to artificial intelligence and agritourism development.
A total of 146 questionnaires were physically administered to the agritourism industry in Zimbabwe.
Eighty-four percent .
%) of the distributed surveys were returned and deemed Table 1 represents the demographic data of the respondents by age, marital status, and level of METHODS This study adopted a positivist philosophy.
Positivism was used as it relies on the hypotheticodeductive method to verify priori hypotheses that are often stated quantitatively, where functional relationships can be derived between causal and explanatory factors .
ndependent variable.
and outcomes .
ependent variable.
(Park et al.
, 2.
It was decided to use a cross-sectional survey because it provides an assessment of exposure and Indonesian Journal of Community Services, 4 .
, 53-60 were between 30 and 39 years old.
Given that 68% of the respondents were women with only 32% being male, it is implied that women are more active in agritourism as they age.
These results are consistent with research conducted in Kenya by (Uduji et al.
, 2.
They discovered that women began to consider launching a business in their late There may be various reasons for it such as lack of support from family, need to look after extended family, involvement in other jobs, child care, and others may be few of them.
Table 1 shows that 52.
6% of the respondents had below diploma level of education.
Another 6% of them indicated that they have attained a diploma education level while 8.
7% of them had Only 2.
2% of them had a masterAos level of Arguably it can be deduced that a good percentage of agritourism entrepreneurs in Zimbabwe have attained basic education.
Convergent Validity Test Results Measuring model fit indices, composite reliability, standardized factor loadings, individual item reliabilities .
quared multiple correlation.
, critical ratios, and average variance extracted (AVE) were used to evaluate convergent validity.
Constructs, standardized factor loadings, and critical ratios are shown in Table 2.
Table 1.
Demographic data Characteristic Age Less than 30 30 Ae 39 40 Ae 49 50 Ae 59 59 and above Total Sex Female Male Total Education Below diploma level Diploma Degree Masters Total Source: Authors .
Table 1 results indicate that the majority of agritourism owners .
%) belonged to the 50Ae59 age range, with 23% following in the 40Ae49 age Only 2.
4% of the people in agritourism said they were under 30 years old, while 14.
1% said they Table 2.
Constructs, standardized factor loadings, and critical ratios Construct
Items Standardized Factor Loadings Positive organizational factors OF1
OF2
OF3
Positive socio-cultural factors SCF1
SCF2
SCF3
SCF4
Economic factors EF1
EF2
EF3
EF4
Environmental factors EMA1
EMA2
EMA3
EMA4
EMA5
Critical Ratios 139*** 796*** 215*** 464*** 253*** 922*** 922*** 922*** 514*** 564*** 234*** 534*** Indonesian Journal of Community Services, 4 .
, 53-60 Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 17 iterations.
Total variance explicated 79.
Note: - CR is fixed.
*** p < 0.
Source: Authors .
According to Table 2, standardized factor composite reliabilities for all the constructs were loadings and critical ratios were acceptable and the above the minimum cut-off point of 0.
Significant total variance explicated was way above the at p<0.
001, all standardized factor loadings for all standard limit of 60% as postulated by (Platin, items were above the minimum cut-off point of 0.
Thus, convergent validity was achieved.
(Segars, 1.
All critical ratios for the items were The measurement model fit indices, namely, sufficiently large (W.
and significant at po0.
N2/DF-1.
Goodness of Fit Index (GFI)-0.
912, (Segars, 1.
All AVEs for the constructs were Adjusted Goodness of Fit Index (AGFI)-0.
903, greater than the minimum cut-off points of 0.
Normed Fit Index (NFI)-0.
Tucker-Lewis Index (Fornell & Larcker, 1.
as shown in Table 2.
(TLI)-0.
Comparative Fit Index (CFI)-0.
948 Discriminant Validity AVEs and squared inter-construct correlations and root mean square error of approximation (SIC.
were examined to evaluate discriminant (RMSEA)-0.
043 were acceptable.
N /DF should be less than 3, and GFI.
AGFI.
NFI.
TLI, and CFI validity.
Table 2's results demonstrate the presence should be close to 1 while RMSEA should be less of discriminant validity.
Comparable SICs were 07 if the model is to be accepted (Segars, smaller than AVEs (Fornell & Larcker, 1981.
All individual item reliabilities were above Segars, 1.
The results of discriminant validity the minimum cut-off point of 0.
5 (Segars, 1.
and are shown in Table 3.
Table 3.
Mean (M), standard deviation (SD).
AVE and SICC
Construct
PUOT PEOU VIUT EDWE
Positive organizational factors (OF) Positive socio-cultural factors (SCF) Economic factors (EF) Environmental factors (EMA) Note: Diagonal elements in bold represent AVEs Source: Authors .
AGFI-0.
NFI-0.
TLI-0.
CFI-0.
Testing Research Hypotheses Structural equation modelling was conducted RMSEA-0.
Table 4 represents hypothesis in AMOS V22 to test the structural relationships testing results.
proposed in Figure 1 (H1-H.
The structural model displayed acceptable fit (N2/DF=1.
GFI-0.
Table 4.
Hypotheses testing Hypothesis Hypothesised Relationship
SRW CR
Remark
85***
Supported Positive Organisational factorsIe Agritourism 041*** Supported Positive socio-cultural factors Ie Agritourism .
451*** Supported Economic factors Ie Agritourism development 95*** Supported Environmental factors Ie Agritourism development .
Source: Authors .
Indonesian Journal of Community Services, 4 .
, 53-60 Results in Table 4 show that organizational factors, sociocultural factors, economic factors, and environmental factors all have a direct and positive effect on agritourism development.
Therefore.
H1Table 5.
Coefficients of moderated regression model Variable Artificial intelligence Age Artificial intelligence y Age Source: Authors .
H5 were tested using moderated regression As for H5, coefficients for the interaction terms (Artificial intelligence x Ag.
were significant .
<0.
This suggests that age moderates the effect artificial intelligence has on agritourism development.
Thus.
H5 was supported.
H4 were supported.
The results imply that artificial .
rganizational, socio-cultural, economic, and environmental factor.
has a positive effect on agritourism development.
Beta t-statistic p-value mitigating potential risks and unforeseen In essence, integrating AI into agritourism in Africa demands an integrated strategy that addresses technological, social, cultural, and environmental dimensions.
By tackling these complex issues.
AI has the potential to enhance the resilience, inclusivity, and sustainability of agritourism in the region.
However, realizing this promise requires a careful balance between technological innovation and deep consideration of ethical, social, and political factors.
Practical implications The integration of AI into agritourism in Africa has significant practical implications for all Key areas include the urgent need for focused training and capacity-building programs to encourage farmers and agritourism businesses to adopt AI technologies.
Crucially, robust infrastructure development, including reliable electricity and internet access in rural areas, is essential for successful AI implementation, alongside facilities for data collection and processing to enable AI-driven decision-making.
Governments must also establish clear regulations addressing data privacy, cybersecurity, and ethical AI use to ensure transparency and accountability.
Furthermore, promoting community engagement and inclusivity is vital, ensuring local communities actively participate in decision-making and benefit from agritourism growth, with a focus on marginalized groups.
AI can also practically enhance market access and promotion through datadriven insights for targeted marketing, personalized recommendations, and market trend analysis, requiring collaboration between technology providers and industry players.
Finally, ensuring environmental sustainability is a practical imperative, using AI to optimize resource use, promote sustainable farming, and monitor the CONCLUSIONS The findings of the study have theoretical, practical, and future research implications.
Theoretical implications The theoretical implications of applying artificial intelligence (AI) to agritourism development in Africa are multifaceted, particularly concerning digital and social media marketing.
Key challenges include bridging the digital divide, ensuring data accessibility and privacy, and maintaining cultural sensitivity.
While AI can enhance marketing, the lack of adequate infrastructure in many rural areas raises concerns about equitable benefit distribution and potentially widening existing disparities.
The long-term success of agritourism hinges on environmental AI offers data-driven insights to optimize resource allocation and minimize ecological impact.
However, the environmental footprint and energy consumption of AI technology itself must be carefully considered.
For AI to be effectively integrated, there's a critical need for skills and capacity development.
This requires investing in educational and training initiatives to equip local communities and stakeholders with both specialized AI knowledge and essential critical thinking and ethical judgment skills.
Governments and regulatory agencies play a crucial role in guiding AI research and ensuring its benefits are equitably distributed.
This involves establishing legal frameworks that foster innovation while Indonesian Journal of Community Services, 4 .
, 53-60 environmental impact of AI adoption in agritourism, with mitigation plans in place for any negative outcomes.
By addressing these practical considerations, stakeholders can leverage AI to drive equitable, eco-friendly, and robust agritourism development in Africa while navigating the unique challenges and opportunities of this technological Future research Among the numerous topics developing in artificial intelligence and agritourism development, future research can concentrate on artificial intelligence (AI) enabled precision agriculture, smart agritourism destinations, ethical and governance challenges, innovation ecosystems and partnerships, social and cultural impacts, and others.
Researchers, policymakers, industry stakeholders, and members of civil society can help shape a more resilient, inclusive, and sustainable future for agritourism development in Africa by participating in these future discussions.
These discussions will address the ethical, social, and environmental implications of artificial intelligence while leveraging its transformative potential.
Artificial Intelligence (AI):
Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy.
International journal of information management, 57.
Fornell.
, & Larcker.
Evaluating structural equation models with unobservable variables and measurement error.
Journal of Marketing Research, 18.
, 39-50.
Karthik.
, & Gajanand.
Agri-Tourism:
An Overview.
Biotech Articles.
Nkosana.
, & Skinner.
A tripartite model for effective marketing of remote micro-hospitality establishments.
African Journal of Hospitality.
Tourism and Leisure.
Park.
Konge.
, & Artino Jr.
The positivism paradigm of research.
Academic medicine, 95.
, 690-694.
Platin.
The relationship between entrepreneurial orientation and performance.
evidence from Turkish SMEs.
Business and Management Studies, 3.
, 78-89.
Segars.
Assessing the unidimensionality of measurement: A paradigm and illustration within the context of information systems research.
Omega, 25.
, 107-121.
Shan.
Chen.
, & Lin.
When social media influencers endorse brands: the effects of self-influencer congruence, parasocial identification, and perceived endorser motive.
International Journal of Advertising, 39.
, 590-610.
Sharma.
Chen.
, & Liu.
Ecoinnovation in hospitality research .
: a systematic review.
International Journal of Contemporary Hospitality Management, 32.
, 913-933.
Tew.
, & Barbieri.
The perceived benefits of agritourism: The providerAos Tourism Management, 33.
Uduji.
Okolo-Obasi.
, & Asongu.
Analysis of farmersAo food price volatility and NigeriaAos growth enhancement support scheme.
African Journal of Science.
Technology.
Innovation and Development, 13.
, 463-478.
REFERENCES