1. Introduction
The study of consumer behavior focuses on understanding human needs and the factors that shape purchasing decisions [
1,
2]. Over time, various theoretical models have been developed to explain how consumers make choices and what influences their behaviors. Branding, a key aspect of consumer psychology, plays a significant role in shaping perceptions and driving purchasing decisions, particularly for newly introduced products. In the rapidly evolving field of agricultural informatics, technological advancements have greatly improved efficiency, sustainability, and productivity [
3,
4]. As businesses strive to promote innovative agricultural technologies, branding has become a critical factor in influencing consumer adoption. This is especially important for Generation Z, a digitally connected and socially conscious group that prioritizes authenticity, innovation, and sustainability. Brand credibility, trust, and engagement heavily influence their purchasing decisions, making branding a key factor in their willingness to adopt emerging agricultural technologies. However, despite growing research on branding and consumer behavior, there is limited understanding of how branding impacts Generation Z’s purchase intentions within the agricultural technology sector. While previous studies have explored branding’s role in digital commerce and consumer decision-making, little research has examined its interaction with the unique behavioral traits of Gen Z in agricultural informatics. This gap is particularly pronounced in rural areas, where adoption of agricultural technology is essential for improving productivity and economic development. Branding strategies for new agricultural technologies must address the specific needs of rural consumers, including affordability, ease of use, and long-term benefits. Unlike urban consumers, who have constant access to digital marketing channels, rural Gen Z consumers may rely more on local influencers, agricultural cooperatives, and government programs when evaluating new technologies. Additionally, trust plays an even more significant role in rural settings, where word-of-mouth recommendations and community-driven decision-making strongly influence purchasing behavior. This study addresses these gaps by analyzing how branding dimensions—such as brand image, trust, knowledge, loyalty, engagement, and experience—affect Generation Z’s intention to purchase newly launched technological products for agricultural use. By incorporating insights from both urban and rural contexts, the research provides valuable recommendations for businesses seeking to market agricultural technologies effectively. The findings contribute to the broader discourse on consumer behavior in the digital age while offering practical strategies for branding technological innovations to meet the needs of Gen Z consumers in both developed and underserved agricultural regions.
2. Literature Review
2.1. Agriculture Management and Gen Z
Agriculture management and informatics are undergoing a rapid transformation with the introduction of newly launched technological products aimed at improving efficiency, sustainability, and productivity [
5]. Innovations such as AI-powered crop monitoring systems, blockchain-enabled supply chain tracking, precision irrigation technologies, and autonomous farming equipment are reshaping traditional agricultural methods by enabling real-time data analysis, predictive insights, and automation [
6]. These advancements are particularly valuable in optimizing resource use, reducing environmental impact, and increasing agricultural yields, making them essential for addressing global food security challenges. Across both urban and rural areas, Generation Z plays a pivotal role in shaping consumer behavior and influencing branding strategies for emerging agricultural technologies. As a generation raised in the digital era, Gen Z relies heavily on online research, social media interactions, and peer recommendations when making purchasing decisions. Their strong preference for transparency, sustainability, and personalized engagement means that agricultural technology brands must emphasize ethical sourcing, environmental responsibility, and interactive digital experiences to build trust and maintain their interest. For rural Gen Z consumers, branding strategies should integrate localized outreach, community-driven demonstrations, and partnerships with agricultural cooperatives to promote these innovations effectively.
Companies introducing new agricultural technologies must focus on brand storytelling, data-driven marketing, and immersive user experiences to establish credibility and foster trust with Gen Z consumers [
7]. Consumer behavior within this demographic is largely shaped by digital engagement, necessitating marketing approaches that extend beyond traditional advertising. Online platforms, AI-driven advisory tools, gamified learning experiences, and interactive virtual farm demonstrations are increasingly being used to educate and influence Gen Z’s purchasing decisions. In rural areas, access to digital resources may be more limited, making field demonstrations, local farmer training programs, and mobile-based advisory services crucial for reaching potential consumers. Additionally, the role of influencers and agricultural content creators continues to grow as social media platforms serve as key channels for product discovery, reviews, and knowledge-sharing. In both urban and rural settings, brands that leverage influencer partnerships, peer recommendations, and digital storytelling are more likely to engage Gen Z consumers who value authenticity, innovation, and community-driven engagement. Recognizing the specific needs of rural consumers—such as affordability, ease of use, and long-term reliability—can help businesses tailor branding strategies to ensure widespread adoption of agricultural technology across diverse demographics.
2.2. Gen Z and Consumer Behaviour
Studying Generation Z’s consumer behavior is crucial for businesses and researchers due to this generation’s growing influence on global markets and its distinct characteristics [
8]. According to Bandara and Liyanage [
9], Generation Z encompasses individuals born between 1995 and 2010, meaning that this group has grown up in a fully digital world with no experience of the pre-digital era. Unlike previous generations, Generation Z has been shaped by rapid advancements in information technology, leading to significant shifts in consumer behavior [
10]. As this generation is still maturing and its preferences continue to evolve, understanding the factors that shape its consumer behavior is essential for businesses to develop effective marketing strategies and adapt their products and services accordingly [
11,
12]. Reaching and engaging Generation Z is essential for businesses to maintain their connection with an ever-changing consumer base [
13]. Companies must remain adaptable to sustain their relevance and competitive advantage in the market [
14]. This generation primarily interacts with brands through online platforms, redefining traditional marketing approaches, consumer engagement strategies, and service delivery models [
15]. Additionally, Generation Z strongly emphasizes values such as sustainability, inclusivity, and authenticity, prompting businesses to innovate and align their offerings with these expectations [
16]. In 2024, Generation Z accounts for approximately 25–30% of consumers, making it the largest demographic cohort [
17]. Generation Z’s current purchasing power is estimated at $360 billion [
18]. As its members advance in their careers and achieve higher incomes, their spending capacity will continue to rise [
19]. By 2030, Generation Z is projected to become the dominant demographic in terms of purchasing power [
20]. Failing to acknowledge this shift could significantly impact the long-term sustainability and growth of businesses across industries [
16]. To ensure future business success, prioritizing Generation Z in both market research and business strategies is essential, providing valuable insights for all stakeholders [
21]. Beyond its direct spending capacity, Generation Z also significantly influences household purchasing decisions, further amplifying its role in shaping consumption trends [
22]. Understanding the behaviors and preferences of this generation enables businesses to craft targeted strategies that not only address their current needs but also anticipate future market trends [
23]. Additionally, insights into Generation Z’s preferences guide companies in leveraging emerging digital trends, such as social commerce and influencer marketing, ensuring their continued relevance in an ever-evolving consumer landscape [
15].
2.3. Newly Launched Technological Products in Agriculture Management
The introduction of new products is essential for business growth and long-term sustainability, particularly in agriculture, where technological advancements are reshaping traditional farming methods. However, predicting the success of these innovations remains a significant challenge, as failed product launches can lead to substantial financial losses. Understanding consumer behavior, particularly among Generation Z, is critical in identifying market trends and optimizing the adoption of technological solutions in agriculture management and informatics. As a digitally native and tech-savvy generation, Generation Z actively seeks innovation and prefers products that integrate seamlessly into their fast-paced, interconnected lifestyles. While this is often associated with smartphones, wearables, and smart home devices, it also applies to emerging agricultural technologies such as precision farming tools, AI-powered crop monitoring systems, smart irrigation solutions, and blockchain-based food traceability platforms. These innovations align with their values of automation, sustainability, and efficiency, all of which are crucial in modern agricultural management. However, in rural areas, where access to digital resources and technological infrastructure may be limited, companies must develop tailored branding strategies to ensure that new agricultural technologies reach and resonate with young farmers and agribusiness professionals. Social media and peer recommendations play a crucial role in shaping Generation Z’s perceptions and adoption of new technological products. This generation relies heavily on digital influencers, online reviews, and interactive marketing campaigns when making purchasing decisions. In agricultural technology, brands introducing drone-based crop monitoring systems, AI-powered soil analysis tools, or IoT-enabled farm sensors can enhance engagement by utilizing video demonstrations, real-user testimonials, and partnerships with agricultural influencers or sustainability advocates. In rural communities, branding efforts should incorporate localized outreach strategies, such as working with agribusiness cooperatives, local farmer associations, and government-supported agricultural extension programs to build trust and encourage adoption.
Additionally, Generation Z values personalized and immersive brand experiences, expecting new products to align with their specific needs and ethical values. In agriculture, this translates to customizable farm management software, AI-driven decision support systems, and sustainability-focused innovations that enhance productivity while minimizing environmental impact. Smart farming applications offering real-time analytics, climate adaptation strategies, and automated resource management appeal to their preference for technology-driven, efficient, and responsible consumption. In rural settings, where hands-on learning is often necessary, brands could implement mobile training units, field demonstrations, and digital education programs to help young farmers adopt and utilize these technologies effectively. Sustainability and ethical business practices are also key drivers of Generation Z’s purchasing decisions. This generation actively supports brands that prioritize carbon footprint reduction, waste minimization, and eco-friendly production processes. In the agricultural sector, biodegradable sensor systems, solar-powered irrigation technologies, and AI-based resource optimization tools align with these values. Companies emphasizing transparent sourcing, fair trade practices, and ethical supply chains in their branding efforts are more likely to gain Generation Z’s trust and loyalty. In rural areas, branding should focus on long-term economic and environmental benefits, demonstrating how smart agricultural solutions can lead to cost savings, increased productivity, and improved sustainability for local farming communities. The demand for speed, convenience, and seamless digital experiences also influences how Generation Z interacts with newly launched products. This expectation applies to agriculture management tools, where cloud-based farm management software, AI-powered pest detection, and blockchain-enabled supply chain tracking must offer user-friendly interfaces, mobile accessibility, and efficient customer support. Companies that fail to meet these expectations risk disengagement, as Generation Z has a low tolerance for inefficiency and outdated technology. In rural areas, where connectivity challenges exist, brands should consider offering offline functionality, SMS-based advisory services, and AI-driven chatbots that work on low-bandwidth networks to ensure accessibility.
Electronic word-of-mouth communication also significantly influences Generation Z’s acceptance of new technological products. Positive user experiences and product success stories shared through online forums, social media discussions, and digital reviews can accelerate product adoption, while negative feedback can discourage potential buyers. This is particularly relevant for agricultural innovations, where case studies, peer-reviewed research, and real-time performance data help consumers assess the value of new technologies. In rural settings, personal testimonials from farmers, community demonstrations, and word-of-mouth endorsements from trusted local figures can be even more influential in shaping purchasing behavior. Generation Z’s preference for innovation and exclusivity further shapes their engagement with product launches, particularly in technology-related sectors. In agricultural technology, companies can generate excitement around new products through beta testing programs, early access trials, and gamified learning experiences that appeal to this generation’s desire for hands-on engagement. Strategies such as augmented reality farm simulations, AI-assisted crop modeling, and blockchain-backed digital certifications for sustainable farming practices enhance their interest in agricultural innovation. In rural areas, offering exclusive incentives such as discount programs, government-backed subsidies, and farmer-led pilot projects can further drive adoption. In conclusion, Generation Z’s consumer behavior is driven by a demand for innovation, authenticity, and convenience, strongly influencing their adoption of newly launched technological products in agriculture management. Businesses in this sector must integrate digital engagement, sustainability-driven messaging, and accessible user experiences to align with Generation Z’s expectations. In both urban and rural markets, effective branding strategies should focus on education, digital connectivity, and long-term benefits to drive widespread adoption. By effectively targeting this tech-savvy yet diverse demographic, agritech brands can drive the acceptance of smart farming technologies, AI-powered analytics, and digital supply chain solutions, ultimately transforming agriculture into a more sustainable, efficient, and data-driven industry.
2.4. Branding and Social Exchange Theory
The Social Exchange Theory (SET) is a sociological and psychological framework that views social relationships and interactions as processes of exchanging resources—both tangible and intangible, such as support, recognition, love, or information [
24,
25]. According to SET, individuals seek to maximize rewards while minimizing costs in their relationships [
24,
26]. In the context of branding, consumers are likely to develop positive attitudes, emotions, and behaviors toward a brand when they perceive that the relationship offers valuable benefits in return [
27]. Within SET, social exchanges often involve implicit obligations, where one party—such as a brand—provides value to the consumer, fostering an ongoing relationship. The connection between branding and SET is significant, as it illustrates how consumers build brand relationships through value-based exchanges [
28]. Specifically, brand image shapes consumer perceptions by signaling whether a brand can fulfill their needs [
29,
30], while brand trust enhances reliability, reducing perceived risks in the exchange [
31,
32]. When consumers consistently receive value from a brand, they develop brand loyalty, leading to long-term commitment [
33,
34]. Brand knowledge enables consumers to assess the quality of the exchange, influencing their decision-making process [
35]. Meanwhile, brand experience represents the tangible outcome of consumer interactions, determining whether the exchange is perceived positively [
36]. Before an exchange occurs, brand awareness serves as a prerequisite, as consumers must first recognize and acknowledge the brand before engaging with it [
37]. Moreover, when consumers perceive interactions with a brand as beneficial, brand engagement increases, prompting them to actively invest in the relationship [
38]. In summary, Social Exchange Theory provides a valuable lens through which to understand how branding variables contribute to the development and reinforcement of brand-consumer relationships through mutual value exchange. It provides a strong foundation for understanding why consumers form and maintain relationships with brands, emphasizing that individuals engage in exchanges where perceived benefits outweigh costs.
2.5. Branding and Social Impact Theory
The Social Impact Theory (SIT), introduced by Latane in 1981, explains how others influence individuals in various social contexts and how their behaviors, opinions, and decisions are shaped by this influence [
39,
40]. SIT identifies three primary factors that determine the strength of social influence: the authority, status, or expertise of the influencing source; the physical or emotional proximity between the source and the individual, where closer proximity enhances influence; and the number of influencing sources, where influence increases with more sources but diminishes as the number grows [
39,
41]. According to this theory, social influence is most effective when the influencing sources are numerous, powerful, and close to the individual [
42]. Research has extensively linked SIT to branding, emphasizing how various sources of influence shape the consumer-brand relationship [
43,
44,
45,
46,
47,
48,
49]. A strong and positive brand image enhances the power of influence, reinforcing consumer perceptions of brand value [
46]. When a brand is widely recognized and carries a strong image, it exerts greater influence over consumers, as they are more likely to perceive its offerings as valuable and align with the shared perceptions surrounding it [
43,
46]. This strong brand presence is crucial in attracting and retaining consumers [
44]. Brand trust plays a key role in reinforcing proximity between consumers and a brand. Consumers who trust a brand feel a sense of closeness, which reduces perceived risks in their purchasing decisions and strengthens the brand’s influence over those choices [
48,
50]. A reliable brand is more persuasive, shaping consumer behavior more effectively [
48,
51]. This ongoing trust fosters long-term relationships, ultimately leading to brand loyalty, where consumers remain committed when a brand consistently meets their expectations [
44,
47]. Strong social influence further reinforces this loyalty as consumers feel more connected to the brand [
52]. Prior experiences with a brand also play a crucial role in determining its influence on future consumer decisions. Positive interactions reinforce a brand’s impact, making consumers more likely to engage with it again [
44,
48,
53]. Brand knowledge further enhances this effect by increasing consumer familiarity with a brand’s values, attributes, and quality, fostering a deeper sense of connection and trust [
48]. The more informed consumers are about a brand, the greater its influence on their purchasing decisions [
45].
Similarly, positive brand experiences create immediate emotional connections that shape brand perceptions through real-life interactions [
54]. These experiences strengthen a brand’s influence by making it more relatable and emotionally engaging for consumers. An exceptional brand experience increases both its perceived proximity and overall impact on consumers’ lives [
54]. Brand awareness is another critical factor in social influence, as it determines the number of sources influencing consumer behavior. The more widely recognized a brand is, the more individuals promote or use it, amplifying its impact [
49]. The sheer volume of exposure and interactions enhances the brand’s credibility and strengthens its social presence. Lastly, brand engagement reflects the extent of interaction and involvement consumers have with a brand, further strengthening its influence and proximity. When consumers actively engage with a brand, they feel a stronger connection, making the brand a more significant part of their lives [
49,
55]. The emotional closeness created through engagement intensifies the brand’s impact on consumer behavior [
56].
In conclusion, the Social Impact Theory provides a comprehensive framework for understanding how brands influence consumer behavior through power, proximity, and the number of influencing sources. This perspective highlights the role of various branding elements in shaping consumer perceptions and decisions, demonstrating the importance of social influence in building strong brand-consumer relationships.
2.6. Branding and Consumer Culture Theory
The Consumer Culture Theory (CCT) is a theoretical perspective that examines consumption as a cultural and social phenomenon rather than focusing solely on economic or psychological aspects [
57]. It explores how culture, social interactions, and identity formation shape consumer behavior [
58]. This theory investigates how consumers construct their identities and establish brand connections through their cultural and social environments [
59,
60]. The relationship between CCT and branding, including elements such as brand image, trust, loyalty, knowledge, experience, awareness, and engagement, underscores the idea that brands are deeply embedded in social and cultural contexts [
61,
62,
63]. CCT highlights the role of brands as cultural symbols that influence consumer choices through shared social and cultural values [
64]. The brand image reflects a consumer’s cultural identity, as individuals select brands that align with their beliefs and social status [
65]. Consumers perceive brands as representations of their social identity and gravitate toward those that resonate with their cultural background and personal values [
66]. Brand trust is reinforced when a brand aligns with consumers’ ethical and cultural standards, fostering a sense of reliability and authenticity [
67]. CCT emphasizes the importance of brands maintaining authenticity and consistency within their cultural sphere, enhancing consumer trust and long-term relationships. In the context of Consumer Culture Theory (CCT), brand loyalty develops when consumers strongly identify with a brand and integrate it into their social identity [
68]. Consumers remain loyal to a brand for its functionality and because it conveys cultural and social messages that align with their values [
57]. CCT explains how cultural identity plays a key role in fostering long-term loyalty toward brands that promote shared beliefs and traditions. Similarly, brand knowledge goes beyond awareness of a product’s features to encompass understanding the cultural meanings a brand conveys [
69]. Consumers perceive brands as carriers of symbolic messages, forming deep knowledge about the values and narratives they communicate [
70]. Brand experience is also shaped by cultural narratives, as consumer interactions with a brand are influenced by broader social and cultural contexts [
71]. Personal experiences with a brand are not isolated events but are instead linked to the consumer’s cultural background and collective identity [
72]. Through the lens of CCT, brand experience is understood as an interaction that strengthens the consumer’s cultural identity [
73]. Brand awareness within CCT is closely tied to a brand’s ability to integrate into cultural movements and social discussions, which enhances its presence in collective consciousness [
74]. The more effectively a brand aligns with cultural trends and social issues, the greater its recognition and relevance [
75]. CCT suggests that brand awareness grows as brands actively participate in cultural conversations and shape social narratives. Finally, brand engagement represents consumers’ active participation in a brand’s cultural expression. It reflects how individuals see themselves as part of a larger community that shares the brand’s values [
76,
77]. Within the framework of CCT, brand engagement is not merely about interaction with a product; it is about consumers identifying with a brand’s cultural significance and becoming active contributors to its social meaning [
78]. In conclusion, Consumer Culture Theory demonstrates that brands are not merely commercial entities but cultural symbols that shape and reflect consumer identities. Brands influence consumer behavior by integrating themselves into cultural narratives, fostering deeper connections through shared values, and reinforcing social identities in ways that extend beyond traditional marketing strategies.
2.7. Purchase Intention and Theory of Reasoned Action
The Theory of Reasoned Action (TRA) is a psychological framework explaining the connection between an individual’s attitudes and behavior [
79]. This theory posits that a person’s behavior can be anticipated based on their intention to engage in that behavior [
80]. Intention, in turn, is shaped by two primary factors: attitude toward the behavior and subjective norms [
81]. Attitude refers to an individual’s personal assessment of whether a behavior is favorable or unfavorable, while subjective norms pertain to the social influence exerted on the individual, specifically their perception of how others view the behavior [
82]. TRA suggests that purchase intentions, social influences, and brand-related beliefs influence consumer behavior, especially in online and social media environments [
83]. As a result, social media marketing and digital engagement play a significant role in shaping consumers’ purchasing decisions [
84]. The Theory of Reasoned Action has been closely associated with key variables such as purchase intention [
85,
86,
87,
88,
89,
90,
91]. Within this framework, purchase intention acts as the strongest predictor of a consumer’s actual buying behavior [
92]. This intention is influenced by both the consumer’s attitude toward the product or brand and their perception of social expectations regarding their purchasing decisions [
86]. The more positive the consumer’s attitude toward a brand and the stronger the perceived social pressure to purchase, the greater the likelihood that they will develop an intention to buy [
93,
94]. In summary, TRA provides insight into the process leading from purchase intention to actual consumer behavior and long-term brand loyalty. Initially, purchase intention drives actual purchasing behavior, while post-purchase experiences shape future purchase intentions and contribute to consumer loyalty. Over time, this process strengthens the consumer’s ongoing relationship with the brand. Based on this theoretical foundation, illustrates the study’s conceptual framework.
In conclusion, this research aims to provide new findings and insights into the understanding of online purchase intention, newly launched technological products that have applications in the agriculture sector and Generation Z. Based on this, the following research hypotheses were formulated, which will be answered through the research process.
H1: Which dimensions of branding exhibit direct statistically significant correlations with the online purchase intention of newly introduced technological products related to the agricultural sector?
H2: Which dimensions of branding can predict the online purchase intention of newly introduced technological products related to the agricultural sector?
3. Methodology
3.1. Research Method
Consumer behavior has been studied using qualitative, quantitative, and mixed-method research approaches. This study adopts a quantitative research methodology to generate measurable and comparable findings. The research is designed as a cross-sectional study, a method commonly used to analyze relationships between variables or to provide insights into population characteristics such as demographics, behaviors, and attitudes [
95].
3.2. Sampling and Participants
This study focused on Generation Z as the target population, specifically individuals born between 1997 and 2012, who are recognized for their digital proficiency, technological experience, and significant influence as consumers. However, to address ethical considerations and avoid the requirement of obtaining parental or guardian consent, individuals under the age of 18 were excluded from participation. This exclusion represents a limitation in fully capturing the perspectives of the entire Generation Z cohort. The research sample consisted of 302 participants, surpassing the minimum required sample size estimated using G-Power analysis. To ensure a statistical power of 0.80, with a significance level of α = 0.05 and medium-to-large effect sizes, the analysis determined that a sample of 160–180 participants would be sufficient for the intended statistical tests. The study employed convenience sampling as the primary method, complemented by systematic sampling techniques to enhance representativeness. The sample was selected based on accessibility, leveraging the researcher’s ability to reach participants efficiently. However, systematic sampling elements were incorporated at certain stages to minimize potential bias. To prevent the influence of alphabetical sorting, participants were chosen from a sampling frame where individuals were recorded without a specific order, and selection occurred at odd-numbered positions (1, 3, 5, 7,
etc.). The choice of convenience sampling was driven by practical benefits, including ease of access, efficiency, and the ability to quickly collect responses from a highly active population in digital environments, social media, and online platforms. Given Generation Z’s familiarity with online interactions and willingness to participate in digital surveys, this method proved to be both cost-effective and suitable for the study. The inclusion of systematic sampling elements further strengthened the research design by reducing selection bias and increasing the reliability of findings. This hybrid sampling approach ensures that the sample reflects the diversity of behaviors and attitudes within Generation Z, making it a robust choice for analyzing consumer behavior in a digitally driven and rapidly evolving demographic. presents the demographic characteristics of the sample. The results show that 52.3 percent of respondents were women, while 85.4 percent were university students. Regarding financial background, 41.7 percent reported a family income between 10,000 and 20,000 euros. The average age of participants was 20.52 years, with a standard deviation of 2.35.
.
Demographics.
Variable |
|
Percentage |
Gender |
Male |
47.7 |
Female |
52.3 |
Educational level |
High school |
0.7 |
University Student |
85.4 |
Bachelor’s degree |
11.2 |
Masters’ degree |
2.7 |
Family income |
<10,000 euro |
19.2 |
10–20,000 euro |
41.7 |
>20,000 euro |
39.1 |
Age |
Mean |
SD |
20.5298 |
2.35068 |
3.3. Research Tool and Data Collection
The primary research instrument for this quantitative study was a structured questionnaire designed to examine various aspects of consumer behavior, particularly regarding Generation Z’s purchasing decisions for newly launched technological products. The questionnaire was divided into three sections, each addressing key areas, including demographic characteristics, purchase intention, and brand-related variables, ensuring respondents a clear and organized format. A pilot version of the questionnaire was tested to refine the wording of the questions, enhancing clarity and relevance to guarantee high-quality data collection. During this pilot phase, discussions with participants were conducted to evaluate the comprehensibility, appropriateness, and relevance of the questions. The questionnaire included demographic variables such as age, gender, education level, and family income to provide a comprehensive profile of the participants. To measure perceptions, attitudes, and behavioral intentions, the questionnaire utilized 5- or 7-point Likert scales, featuring verbal anchors such as “Strongly Disagree—Strongly Agree” or “Not at all—Very much”. All variables used in this study, including both the dependent variable (purchase intention) and the independent branding-related constructs (such as online brand experience, brand knowledge, brand image, and brand trust), are measured on continuous scales. These variables were operationalized using Likert-type items, allowing for the application of parametric statistical techniques suitable for analyzing continuous data. The data gathered through this structured tool facilitated in-depth statistical analyses, offering valuable insights into consumer behavior patterns related to new technological product adoption. One of the key strengths of the research tool was the incorporation of well-established measurement scales, widely used in previous studies to ensure the validity and reliability of the findings, as summarized in .
.
Research scales.
Variable |
Source |
Brand behavioural intention |
[96,97] |
Online brand engagement |
Adapted from Rather & Hollebeek [96] |
Online brand experience |
[96,98] |
Brand awareness |
Adapted from Yoo, Donthu & Lee [99] |
Brand trust |
[100,101] |
Brand knowledge |
Adapted from Hanaysha [102]; Jin, Lee & Huffman [103] |
Brand image |
Adapted from Hanaysha [102]; Jin, Lee & Huffman [103] |
Brand loyalty |
Adapted from Hanaysha [102]; Jin, Lee & Huffman [103] |
Purchase intention |
[104,105] |
The questionnaire incorporated well-established measurement scales to enhance the reliability of responses. After data collection, rigorous validation procedures were implemented to assess both the validity and reliability of the research tool. These procedures, which are elaborated in the following chapter, included confirmatory factor analysis (CFA), outlier detection, reliability testing, and normality checks. Confirmatory factor analysis was conducted to verify the structure of the scales, ensuring they accurately measured the intended constructs. Reliability analysis assessed the internal consistency of the scales, while outlier detection and normality tests ensured the statistical soundness of the dataset. Once the data were confirmed as valid and reliable, both descriptive and inferential statistical analyses were performed to examine the research hypotheses, ensuring that the study produced accurate and meaningful insights. Descriptive statistical analysis, presented in the form of charts and tables, provided an overview of the responses across all variables, offering a clear depiction of the sample’s characteristics and trends. Inferential statistical techniques were applied, including correlation analysis, multiple regression, and moderation analysis, to test the relationships between key variables and evaluate the proposed hypotheses. Ethical considerations were carefully followed throughout the research process, adhering to the principles of the Declaration of Helsinki. Participation in the study was entirely voluntary, with informed consent obtained from all respondents, and strict measures were taken to protect their privacy and confidentiality [
106]. The reliability analysis demonstrated that most of the study’s variables exhibited strong internal consistency, as indicated by Cronbach’s Alpha values (). The set of variables used in the study showed excellent reliability, confirming the accuracy and consistency of the measurements. Overall, the results validate the questionnaire as a reliable and effective instrument for achieving the research objectives, providing a robust foundation for the subsequent analyses and conclusions.
.
Reliability analysis.
Κλίμακα |
Cronbach’s Alpha |
Items |
Brand Image |
0.836 |
5 |
Brand Trust |
0.866 |
5 |
Brand Loyalty |
0.858 |
5 |
Brand Awareness |
0.739 |
4 |
Brand Behavioral Intention |
0.754 |
2 |
Purchase intention |
0.801 |
5 |
Online Brand Experience |
0.892 |
12 |
Online Brand Engagement |
0.905 |
9 |
Brand Knowledge |
0.912 |
18 |
4. Results
4.1. Correlations of the Brand Dimensions and Online Purchase Intention
presents Spearman’s rho correlation coefficients, illustrating the relationships between purchase intention and various branding-related factors. The results indicate both the strength and direction of these relationships, with all branding dimensions showing statistically significant positive correlations with purchase intention (
p < 0.01). This suggests stronger branding attributes are generally associated with a higher likelihood of purchasing newly introduced technological products in the agricultural sector. Among the branding dimensions, online brand experience (r = 0.452) demonstrates one of the strongest correlations with purchase intention, highlighting the critical role of digital interactions in influencing consumer buying behavior. Similarly, brand engagement (r = 0.419) exhibits a strong positive correlation, indicating that consumers who actively interact with and emotionally connect to a brand are more inclined to make a purchase. Brand image (r = 0.382) also plays a key role, suggesting that consumer perceptions of a brand’s reputation and positioning significantly impact purchase decisions. Brand trust (r = 0.240), while still statistically significant, has a weaker correlation than other factors, implying that although trust influences purchase intention, it may not be the most decisive factor. Brand loyalty (r = 0.364) suggests that fostering long-term commitment among consumers positively influences purchase decisions. Brand awareness (r = 0.234) exhibits the weakest correlation, indicating that simple brand recognition is not a strong driver of purchase behavior—factors such as engagement and experience hold greater importance. Brand behavioral intention (r = 0.256) shows a moderate relationship with purchase intention, demonstrating that consumers’ behavioral commitment to a brand influences their likelihood of making a purchase. Lastly, brand knowledge (r = 0.395) positively correlates with purchase intention, reinforcing that well-informed consumers are likelier to buy. Overall, these findings emphasize the complex role of branding in shaping purchase intention for newly launched technological products. While all branding dimensions are significantly correlated with purchase intention, online brand experience, engagement, and knowledge emerge as the most influential factors. In contrast, though still relevant, brand awareness and trust show relatively weaker correlations, reinforcing the idea that mere brand recognition is insufficient. Instead, active engagement, positive experiences, and knowledge dissemination are more effective in driving actual purchasing decisions. These insights highlight the importance of agricultural technology brands focusing on delivering meaningful digital experiences, fostering consumer engagement, and educating their audience to enhance purchase intention.
.
Correlations of the Brand Dimensions and Online Purchase Intention.
|
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
Spearman’s rho |
1. Purchase intention |
- |
0.452 ** |
0.419 ** |
0.382 ** |
0.240 ** |
0.364 ** |
0.234 ** |
0.256 ** |
0.395 ** |
2. Online Brand Experience |
|
- |
0.692 ** |
0.593 ** |
0.371 ** |
0.468 ** |
0.360 ** |
0.405 ** |
0.598 ** |
3. Brand engagement |
|
|
- |
0.611 ** |
0.425 ** |
0.583 ** |
0.465 ** |
0.544 ** |
0.674 ** |
4. Brand image |
|
|
|
- |
0.438 ** |
0.532 ** |
0.571 ** |
0.505 ** |
0.825 ** |
5. Brand trust |
|
|
|
|
- |
0.509 ** |
0.495 ** |
0.415 ** |
0.738 ** |
6. Brand loyalty |
|
|
|
|
|
- |
0.555 ** |
0.670 ** |
0.840 ** |
7. Brand awareness |
|
|
|
|
|
|
- |
0.501 ** |
0.740 ** |
8. Brand behavioral intention |
|
|
|
|
|
|
|
- |
0.669 ** |
9. Brand knowledge |
|
|
|
|
|
|
|
|
- |
4.2. Regression Analysis for the Prediction of Online Purchase Intention
presents a summary of the prediction model for online purchase intention, providing key statistical measures to evaluate its performance. Model 1 reports a correlation coefficient (R) of 0.562, indicating a strong association between the independent variables and online purchase intention. The R Square value of 0.316 suggests that approximately 31.6% of the variation in online purchase intention can be explained by the predictors included in the model. The Adjusted R Square value of 0.297 accounts for the number of predictors, refining the R Square estimate to provide a more precise measure of the model’s explanatory power. The standard error of the estimate is 0.74485, representing the average deviation of observed values from the predicted values. Additionally, the Durbin-Watson statistic is 1.562, which is close to the optimal value of 2, indicating minimal autocorrelation in the residuals. This suggests that the model provides a reliable fit for the data, reinforcing its validity in predicting online purchase intention.
.
Regression model summary.
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
Durbin-Watson |
1 |
0.562 |
0.316 |
0.297 |
0.74485 |
1.562 |
presents the findings of a multiple regression analysis that explores the impact of different branding dimensions on purchase intention. The unstandardized coefficients (B) indicate the direct effect of each independent variable on purchase intention, while the standardized coefficients (Beta) allow for a comparison of the relative influence of each factor. The
t-values and
p-values assess statistical significance with a threshold of
p < 0.05. The constant term (B = 1.315,
p < 0.001) suggests that even when all branding variables are set to zero, purchase intention remains positive, implying that additional factors beyond the model contribute to consumer buying behavior. Among the branding dimensions, online brand experience (B = 0.191,
p < 0.001) has a significant positive impact on purchase intention, with a moderate Beta value of 0.270. This highlights the importance of well-structured and engaging digital interactions in influencing consumers’ willingness to purchase, aligning with trends in digital marketing where a seamless online experience enhances brand appeal. Brand engagement (B = 0.072,
p = 0.158), however, does not exhibit a statistically significant effect on purchase intention. This suggests that while consumers may actively engage with a brand, such interaction alone does not necessarily translate into purchasing behavior. In contrast, brand image (B = 0.910,
p = 0.002) emerges as one of the strongest predictors of purchase intention, with a high Beta value (0.996), emphasizing the role of a positive brand perception in driving consumer choices. Brand trust (B = 0.801,
p = 0.005) also shows a significant relationship with purchase intention, indicating that consumers who perceive a brand as reliable and credible are more inclined to make a purchase. Similarly, brand loyalty (B = 0.922,
p = 0.001) is a key predictor, with the highest Beta value (1.044), reinforcing the idea that consumers who have an emotional connection to a brand are more likely to continue purchasing from it. Brand awareness (B = 0.351,
p = 0.044) has a significant but relatively weaker effect on purchase intention, suggesting that while brand recognition contributes to purchasing decisions, it is not as influential as trust and loyalty. This implies that merely recognizing a brand is insufficient to drive purchases unless other positive brand attributes accompany it. Brand behavioral intention (B = 0.022,
p = 0.593) does not show a significant effect on purchase intention, indicating that general behavioral tendencies toward a brand do not necessarily lead to an actual purchase. However, brand knowledge (B = 2.736,
p = 0.006) has a substantial positive impact, with a high Beta value (2.240). This finding suggests that well-informed consumers are more likely to make a purchase, particularly in the agricultural technology sector, where understanding a brand’s offerings and credibility can increase purchasing confidence. Contrary to some previous research suggesting that extensive brand knowledge may lead to skepticism, this study indicates that consumers with a strong knowledge of a brand are more assured in their purchasing decisions.
.
Coefficients of the Prediction Model for Online Purchase Intention.
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
p |
B |
SE |
Beta |
1 |
(Constant) |
1.315 |
0.249 |
|
5.279 |
<0.001 |
Online Brand Experience |
0.191 |
0.050 |
0.270 |
3.828 |
<0.001 |
Brand engagement |
0.072 |
0.051 |
0.110 |
1.415 |
0.158 |
Brand image |
0.910 |
0.292 |
0.996 |
3.115 |
0.002 |
Brand trust |
0.801 |
0.285 |
0.721 |
2.814 |
0.005 |
Brand loyalty |
0.922 |
0.279 |
1.044 |
3.304 |
0.001 |
Brand awareness |
0.351 |
0.173 |
0.306 |
2.027 |
0.044 |
|
Brand behavioral intention |
0.022 |
0.041 |
0.038 |
0.535 |
0.593 |
|
Brand knowledge |
2.736 |
0.985 |
2.240 |
2.779 |
0.006 |
Based on the final model, in which all factors are included as independent variables, the following regression equation was developed for Online Purchase Intention. Specifically, for each unit increase in an independent variable, the dependent variable (online consumer behaviour) increases by the amount of b, assuming all other variables remain constant.
Overall, the findings emphasize the significant influence of brand image, trust, loyalty, and online brand experience on purchase intention, whereas factors such as engagement and behavioral intention show minimal or no direct impact. The results indicate that businesses should prioritize enhancing their brand image, building strong customer trust, and improving the online brand experience to encourage consumer purchases. Furthermore, increasing brand knowledge among potential customers emerges as a valuable strategy, as well-informed consumers tend to develop greater confidence in a brand, making them more likely to complete a purchase.
5. Discussion
The study examines the impact of branding dimensions on Generation Z’s purchase intention of newly introduced technological products in the agricultural sector. The findings highlight the critical role branding plays in shaping consumer attitudes and behaviors, particularly among a digitally native and socially conscious demographic. This discussion places the study’s key findings within the context of existing literature, explores their implications for businesses, and suggests directions for future research. The results indicate that online brand experience is one of the strongest predictors of purchase intention among Generation Z consumers in the agricultural technology sector. This aligns with prior research suggesting that digital interactions significantly influence consumer behavior [
30,
38]. The prominence of online brand experience underscores the increasing reliance of younger consumers on virtual brand interactions, particularly in industries such as agriculture management, where digital tools, online demonstrations, and virtual consultations are becoming essential for marketing and consumer engagement [
37]. In this sector, technology-driven experiences such as AI-powered farm management platforms, virtual field trials, and blockchain-based transparency initiatives may enhance trust and engagement, ultimately shaping purchase intentions.
Brand knowledge also emerged as a significant predictor of purchase intention, reinforcing that well-informed consumers are more confident in purchasing decisions. This is consistent with Keller’s [
107] framework of brand knowledge, which highlights the importance of awareness and associations in influencing consumer choices. However, this study extends previous research by demonstrating that brand knowledge plays an even greater role in the agricultural technology sector, likely due to the complexity and perceived risk of adopting advanced farming innovations. Given that new agricultural technologies often require a significant investment and learning curve, consumers need comprehensive and accessible information to reduce uncertainty and increase adoption rates. This finding suggests that educational marketing strategies, such as webinars, product demonstrations, expert testimonials, and interactive content, are essential for effectively promoting agricultural technology products to Generation Z farmers and agribusiness professionals. Brand trust, while statistically significant, exhibited a weaker correlation with purchase intention compared to brand experience and knowledge. This contrasts with earlier studies emphasizing trust as a dominant factor in consumer decision-making [
31,
32]. The relatively lower impact of brand trust suggests that while reliability and credibility remain important, Generation Z consumers in the agricultural sector may prioritize firsthand experience and product knowledge over traditional trust-building mechanisms. This shift may be attributed to their data-driven approach to decision-making, where objective performance metrics, peer reviews, and real-time feedback play a more significant role than trust alone. Consequently, brands offering agricultural technology solutions should focus on providing transparent product performance data, field trial results, and user-generated testimonials to build credibility alongside trust.
Brand loyalty was also found to significantly influence purchase intention, confirming previous research that links consumer commitment to repeated purchasing behavior [
108]. However, Generation Z’s brand loyalty appears to be more fluid, unlike older consumer segments, where loyalty is often a dominant predictor of long-term brand success [
61]. This suggests that their purchasing decisions are shaped more by engagement, experience, and innovation rather than long-term attachment to a particular brand. In agriculture management, where technological advancements occur rapidly, this insight underscores the importance of continuous innovation, responsive customer service, and dynamic engagement strategies to maintain consumer interest and brand preference. Brand engagement, while positively correlated with purchase intention, was not a strong predictor in the regression analysis. This contrasts with research suggesting that consumer engagement is a primary driver of purchase behavior [
55]. One possible explanation is that engagement alone does not necessarily translate into purchasing behavior unless it is paired with a compelling brand experience and perceived product value. This insight is particularly relevant to the agricultural technology sector, where engagement strategies should be integrated with experience-driven marketing efforts, such as immersive virtual farm tours, AI-based recommendations, and interactive content demonstrating product applications in real-world farming scenarios.
Brand awareness showed the weakest correlation with purchase intention, suggesting that mere brand recognition is not enough to drive purchasing decisions. This supports previous findings that awareness must be complemented by positive brand associations and engagement to be impactful [
49]. Given that agricultural technology products often lack the widespread brand visibility of mainstream consumer goods, this finding highlights the need for targeted branding efforts that go beyond awareness-building initiatives. Companies in this sector should consider strategic partnerships with agricultural influencers, participation in industry trade shows, and digital campaigns that emphasize their innovations’ practical applications and benefits. The brand behavioral intention had a weaker-than-expected influence on purchase intention, contradicting prior research that links stated intent with actual consumer behavior [
79,
86]. This suggests that for Generation Z consumers, expressing an intent to engage with a brand does not always translate into a purchase. This reinforces the need for tangible brand experiences and informational content that facilitate decision-making and encourage conversion. For agricultural technology brands, this could mean offering risk-free trial periods, live demonstrations, and subscription-based models that allow consumers to experience the product before committing to a purchase.
The findings align with the Social Exchange Theory (SET), which explains the reciprocal value exchange between brands and consumers. The study suggests that consumers reciprocate with loyalty and purchase intent when brands provide tangible benefits such as reliability, efficiency, and sustainability. However, the relatively weaker correlation of brand trust with purchase intention suggests that while trust remains important, it must be reinforced with transparent data, proven performance, and interactive brand experiences to be truly effective in driving purchases. Social Impact Theory (SIT) further contextualizes the role of digital influence in consumer decision-making. The study underscores that peer recommendations, influencer endorsements, and social proof significantly enhance brand credibility and influence Generation Z’s purchasing behavior. This aligns with previous research indicating that digital word-of-mouth and social media engagement are powerful drivers of consumer behavior [
42]. In agriculture management, this insight highlights the potential of community-driven marketing strategies, where early adopters of new technologies can share their experiences through video testimonials, social media discussions, and peer-reviewed content to encourage broader adoption. Brand loyalty emerged as a significant predictor of purchase intention, reinforcing the idea that consistent and positive brand interactions lead to sustained consumer commitment [
108]. However, the relatively weaker influence of brand behavioral intention suggests that while consumers may express an interest in engaging with a brand, this does not always translate into an actual purchase. This discrepancy highlights the need to bridge the gap between interest and action, particularly in the agricultural technology sector. Brands can achieve this by offering interactive decision-making tools, AI-driven consultations, and predictive analytics that help consumers make informed purchase decisions. In conclusion, this study provides valuable insights into how branding dimensions influence Generation Z’s purchase intention of technological products in the agricultural sector. The findings emphasize the importance of online brand experience, brand knowledge, and loyalty, while also highlighting the need for data-driven marketing, interactive brand experiences, and community-driven engagement strategies. As the agricultural industry continues to integrate digital and smart farming solutions, these insights can guide businesses in effectively positioning their products and strengthening consumer relationships in an increasingly competitive and technology-driven market.
6. Conclusions
This study offers an in-depth examination of the impact of branding on Generation Z’s purchase intention for newly introduced technological products in the agricultural sector. The findings emphasize the crucial role of online brand experience, brand knowledge, and brand image as primary drivers of purchase intention. While brand trust and loyalty remain influential, they do not independently dictate consumer behavior without the reinforcement of strong engagement and immersive brand experiences. This research adds to the existing body of literature by showing that Generation Z places greater emphasis on experiential and knowledge-based branding strategies rather than relying solely on traditional trust-building methods. Future studies should further explore the dynamic nature of consumer-brand relationships and examine how external influences shape branding effectiveness in an increasingly digital and technology-driven marketplace.
6.1. Practical Implications
The findings of this study offer valuable insights for businesses and policymakers seeking to engage Generation Z consumers in the agricultural technology sector. The strong correlation between online brand experience and purchase intention highlights the need for businesses to invest in high-quality digital interactions. Companies should develop intuitive platforms, interactive virtual experiences, and AI-driven recommendation systems to strengthen consumer trust and engagement. In agriculture management and informatics, this could involve virtual farm tours, AI-powered decision-making tools, and real-time data visualization dashboards that allow users to experience the benefits of new agricultural technologies before adoption. The importance of brand knowledge as a predictor of purchase intention suggests that educating consumers about the benefits, functionality, and sustainability of agricultural innovations is essential for increasing adoption rates. Businesses should implement educational campaigns using interactive webinars, explainer videos, and data-driven storytelling to build consumer confidence. Companies developing precision agriculture tools, IoT-based farm monitoring systems, or AI-driven pest control solutions should provide detailed case studies, transparent performance data, and peer testimonials to demonstrate real-world applications and effectiveness. The role of brand trust and loyalty in influencing purchase intention highlights the significance of long-term consumer relationships. Agricultural technology companies should prioritize consistent product quality, ethical business practices, and transparent communication to foster loyalty among Generation Z consumers. This can be achieved by integrating blockchain-based traceability systems, sustainable resource management practices, and responsive customer support services that align with Generation Z’s values. Brands that emphasize sustainability, environmental responsibility, and ethical sourcing in their messaging are more likely to connect with this generation and encourage long-term brand engagement.
The study also revealed that brand engagement had no significant effect on purchase intention, indicating that businesses may need to reassess their current engagement strategies. Instead of focusing solely on promotional content, engagement efforts should provide meaningful, value-driven interactions. In agriculture management, this could include participatory digital platforms where young farmers can contribute feedback, share best practices, and collaborate with technology providers. For example, crowdsourced innovation challenges, AI-driven advisory forums, and interactive simulation tools could enhance engagement by involving consumers directly in product development and refinement. From a policy perspective, government agencies and agricultural organizations should support digital literacy programs and initiatives that raise awareness of technological advancements in sustainable farming. Policies that encourage the integration of smart farming technologies in educational curricula could foster early adoption among young consumers. Collaborations between agricultural technology companies, universities, and vocational training centers can provide hands-on experiences such as internships in agri-tech startups, farm-based technology labs, and research partnerships focused on AI-driven sustainability projects. Additionally, policies that make smart agricultural technologies more accessible to young farmers could encourage widespread adoption. Financial incentives, such as low-interest loans for investing in precision agriculture tools, grants for implementing data-driven farm management systems, and government-backed training programs on AI-powered agronomy, could lower entry barriers and promote the use of innovative solutions. Overall, businesses and policymakers should recognize that Generation Z’s adoption of agricultural technology depends not only on product functionality but also on its alignment with their digital preferences, sustainability values, and need for transparency. By incorporating immersive digital experiences, educational outreach, and sustainability-focused branding efforts, companies can strengthen consumer trust and drive long-term engagement in the rapidly evolving agricultural technology sector.
6.2. Research Limitations
Although this study offers valuable insights into Generation Z’s purchase intention for agricultural technology products, certain limitations should be acknowledged. First, the research was based on a cross-sectional design, capturing consumer attitudes at a single point in time. This approach does not allow for an examination of how consumer behavior evolves over-time. Future longitudinal studies could offer a more in-depth perspective on how branding dimensions influence purchase intention in the long term. Second, the study employed a convenience sampling method, which may limit the generalizability of the findings. While systematic sampling elements were integrated to reduce bias, the sample may not fully represent the broader Generation Z population. Future research should consider employing more randomized and diverse sampling techniques to enhance representativeness and ensure that findings apply to a wider audience. Additionally, the study did not account for external factors such as economic conditions, technological advancements, and policy changes, which may influence consumer attitudes and purchasing behaviors in ways not captured by the analysis. Future research should incorporate these variables to provide a more comprehensive understanding of the relationship between branding and consumer decision-making in the agricultural technology sector. Finally, the exclusion of respondents under the age of 18 is an additional limitation of this study, which restricts the scope of insights into the full spectrum of the digital generation, particularly the younger segment of Gen Z. As adolescents are early adopters of technology and often influence household purchasing decisions, their exclusion may result in an incomplete understanding of branding perceptions and consumer behavior within this cohort. Future research should consider incorporating the perspectives of minors—within ethical and parental consent frameworks—to capture a more holistic view of tech adoption patterns across the digital generation.
6.3. Future Research Directions
While this study provides valuable insights into the role of branding in shaping Generation Z’s purchase intention for newly launched agricultural technology products, several areas warrant further exploration, particularly in the context of agriculture management and informatics. Future research could examine how different digital engagement strategies impact brand perception and purchase behavior among Generation Z in the agricultural sector. This could include evaluating the effectiveness of social media influencers specializing in precision farming, gamification techniques such as virtual farm simulations, and interactive marketing campaigns featuring augmented reality experiences. Investigating how these strategies build trust and loyalty in agricultural technology products would provide practical insights for businesses looking to engage young consumers more effectively. Since this study primarily focused on Generation Z, future research could also explore how branding influences other generational cohorts, such as Millennials and Generation Alpha, to identify differences in brand perception and purchasing behavior. A comparative analysis across generations could help businesses understand how younger consumers adopt emerging agricultural technologies compared to older generations, who may have different technological familiarity and risk tolerance levels. This could be particularly useful in determining how different generations respond to innovations such as AI-driven crop monitoring systems, blockchain-based supply chain tracking, and IoT-enabled farm management solutions. Further studies are needed to assess the long-term effects of brand trust and loyalty on customer retention in the agricultural technology sector. Given the rapid evolution of agri-tech solutions, exploring whether initial trust and loyalty translate into sustained consumer engagement over time would be beneficial. Longitudinal research could track how early adopters of precision agriculture tools or smart irrigation systems continue using these technologies and whether brand loyalty is maintained as newer innovations emerge.
Additionally, future research could investigate how external factors such as economic fluctuations, environmental regulations, and global supply chain disruptions influence consumer purchasing decisions in agricultural technology. Understanding the resilience of branding strategies in the face of market uncertainties could help businesses and policymakers develop adaptive marketing approaches that align with evolving consumer expectations. For instance, studying how climate change policies and government subsidies for sustainable farming technologies impact branding effectiveness could offer valuable insights for the sector. As this study was conducted using a quantitative approach, future studies could integrate qualitative methods such as in-depth interviews and focus groups to explore the psychological and emotional drivers behind brand preferences. Collecting narratives from Generation Z consumers involved in digital farming, urban agriculture, or sustainable agribusiness startups could provide richer insights into how branding influences their decision-making processes. Understanding their motivations for adopting smart farming technologies, AI-based pest detection, or automated greenhouse management systems would enhance the depth of branding research in this field. Cross-cultural studies could further investigate whether these findings hold true in different geographic regions and socio-economic contexts. Given the diverse nature of agricultural industries worldwide, research comparing branding strategies in high-tech commercial farming in North America with smallholder adoption of digital tools in developing economies could provide a more global perspective. Identifying cultural differences in branding preferences for sustainable agriculture technologies, digital cooperatives, and direct-to-consumer farm tech solutions would enhance the generalizability of the results. Moreover, future research could benefit from incorporating qualitative methods, such as focus groups or semi-structured interviews, to uncover the deeper psychological and contextual drivers behind tech adoption decisions within the digital generation. These methods would provide rich, detailed insights into how individuals interpret and respond to branding strategies beyond what quantitative measures can capture. Moreover, controlling for potential confounding variables—such as socio-economic background, prior exposure to technology, and cultural influences—would help isolate the true effects of branding elements and enhance the validity of future findings. Overall, future research should focus on expanding the scope of branding studies in agriculture management and informatics, incorporating digital engagement, generational comparisons, external influences, and qualitative insights to better understand consumer behavior in this evolving sector.
Author Contributions
Conceptualization, D.T. and G.T.; Methodology, D.T. and G.T.; Software, D.T.; Validation, D.T.; Formal Analysis, D.T.; Investigation, D.T.; Resources, G.T.; Data Curation, D.T.; Writing—Original Draft Preparation, D.T., G.A. and G.T.; Writing—Review & Editing, D.T., G.A. and G.T.; Visualization, D.T.; Supervision, G.T. and G.A.; Project Administration, G.T.
Ethics Statement
The whole study was conducted according to the guidelines of the Declaration of Helsinki, and approved by Department of Organizations Marketing and Tourism International Hellenic University (IHU) (protocol code 1/7-01-21 and 24 April 2024).
Informed Consent Statement
The study involves human participation and all participants have given a written consent.
Data Availability Statement
The data presented in this study are available on request from the corresponding author.
Funding
This research received no external funding.
Declaration of Competing Interest
There are no conflicts of interest.
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