Abstract
Brands are among the most valuable assets of agricultural businesses. Geographical branding can play a fundamental role in national and international markets by creating a competitive identity. On the other hand, orchard owners in a certain geographical region can understand the status of a product’s supply chain. Nonetheless, few studies have focused on how branding can influence the status of a product’s supply chain. Thus, the present study aimed to analyze the effect of geographical branding on improving the apple supply chain. The research is an applied study in terms of the goal, conducted by the survey methodology. Data were collected by distributing 360 questionnaires among apple orchard owners in Damavand County sampled by simple randomization. Cochran’s formula estimated the sample size. The research instrument was a research-made questionnaire. Data were analyzed by structural equation modeling. According to the results, special brand value, brand loyalty, brand image, brand attitude, brand experience, brand purchasing intention, and brand identity were the components found to improve the efficiency of the Apple supply chain significantly.
1 Introduction
Nowadays, the expansion of industrial and agricultural mass production, trading, marketing, and media has raised a need for marking products for broader markets and differentiating them in the market (Liu and Wang, 2022). Indeed, creating a commercial brand enables firms to generate a specific image of their products among consumers (Ameri Siyahooei et al., 2023). In this respect, branding has been a successful marketing approach for agricultural products, which has helped fill the gap between producers and consumers by identifying the product (Zheng et al., 2022). A successful brand can increase the product price by 5–7% in addition to promoting product quality and competitive advantage by acceptable pricing and distinction from similar products. In case of no branding, farmers’ income and consumer support will be diminished (Essa and Hassen, 2022). If agricultural products cannot be differentiated in the view of consumers, the market will remain product-oriented. Consequently, it can be claimed that all products are perceived to be similar for a consumer and must be priced the same. Product brand guarantees quality improvement, increase competition between producers and retailers, endow farmers with the power of bargaining with many buyers and allow supplying branded products to customers (Ameri Siyahooei et al., 2023). In this regard, geographical branding can effectively enhance international competition for products (Zheng et al., 2022). The term geographical brand points to the origin name and geographical indication. The origin name aims to specify that products with quality and specific features are conditioned with a geographical environment, including natural and human factors, and that they are produced, processed, and prepared completely in a certain region (a state, a region, or a location) (Uzelac et al., 2022). Indeed, geographical branding is used to identify products originating from a certain region and allow for attributing the quality, reputation, or other features of the product to its geographical or production origin (Pashova and Radev, 2021).
Various research studies have revealed that different factors influence the promotion and expansion of geographical brands, e.g., product label and shape (Pashova and Radev, 2021; Chen et al., 2023), place of sale (Uzelac et al., 2022), brand image (Mitchell and Balabanis, 2021), attitude toward brand promotion (Nayeem et al., 2019), special brand value (Niyas and Kavida, 2022), brand loyalty (Lin et al., 2019), perceived brand quality (Ray et al., 2021), attitude toward the brand (Foroudi, 2019), brand identity (Uzelac et al., 2022), and brand personality (Vi et al., 2023).
Research shows that promoting a brand and marketing agricultural products can positively improve its supply chain and the relevant components (Noori et al., 2024). For instance, Modak et al. (2024) argue that branding methods have turned into established aspects of marketing activity that can improve supply chain efficiency. Türkeș et al. (2024) found a positive and significant relationship between supply chain sustainability methods and commercial performance. They reported that commercial performance could be maximized by focusing on promoting economic, social, and environmental sustainability of the supply chain. According to Yarmand et al. (2023), focus on improving marketing strategies is vital for enhancing export performance. Furthermore, sale and marketing managers must use proper distribution channels for their export development. They must fit their product quality and packaging with customer preferences. Essa and Hassen (2022) found that branding opportunities (including enhancing efficiency for the standard production process, increasing product protection against any damage, the primary determinant of customer choice, increasing customer loyalty, fostering competition among brands, and increasing production) contributed to adhering to international standards. They also showed that the trademark of the location where the product has been made would be useful for its branding. Guo and Yin (2022) suggest that the two processes of customer services and customer relation management need the most attention as they can improve production efficiency. Mu et al. (2021) state that the influence of supply chain efficiency on marketing is inevitable. According to Pashova and Radev (2021), a significant fraction of fresh fruits and vegetables are unmarked in the Bulgarian trade network whereas the next ranks are for products with conventional production labels and bio-labels. According to Azaron et al. (2021), supply chain management is a component by which many countries try to achieve the highest level of customer satisfaction. Li et al. (2021) believe that the key to successful marketing of fresh products is to understand the supply chain and how it functions, which can optimize all links and relations. Jung et al. (2021) found it suitable to use the latest advances in remote sensing technology and to use brands for improving the flexibility of agricultural systems. Liao et al. (2021) argue that business leaders tend to adopt various advertising strategies based on their own preference for brands in order to increase supply chain performance. Their research findings showed that investment in brand promotion was relevant to leaders’ attitudes toward brands. According to Escribano et al. (2020), supply chain sustainability must be considered to make crop trademarks more attractive to consumers – especially, younger customers. Also, Gardner et al. (2019) argue that supply chain understanding is necessary not only for farmers but also for all who are involved in harvesting, packaging, and supplying crops to the market and delivering them to customers. According to Ghazinoori et al. (2020), since the market demand is changing, the Internet is mainly integrated with the supply chain, so the government needs to pay more attention to the supply chain and its stability. López-Bayón et al. (2018) also believe that the use of geographical branding as a tool plays an essential role in high-quality supply chain governance and management. Most studies have addressed five criteria of reliability, flexibility, accountability, cost, and asset as the main dimensions of the supply chain (Dissanayake and Cross, 2018; Lima-Junior and Carpinetti, 2020; Nguyen et al., 2021; Jafari et al., 2023).
Iran has a high potential for producing safe and high-quality food, but the realization of this potential requires proper marketing support based on the strategic situation and consumer perceptions during product differentiation (Ghasemi et al., 2019; Maghrebi et al., 2020). The development of specific indicators that are used to promote agricultural products’ added value is important for their producers from various perspectives so that it can make use of developing agricultural product brands, strengthening consumers’ trust, increasing the recognition of the region where the product originates from, protecting unfair competition, and accomplishing the market value (Sugianto et al., 2023). Apples are the third most cultivated horticultural crop after citrus and bananas (Choupannejad et al., 2018). Iran has a special place for apple production in the world thanks to its proper climatic conditions for its cultivation (Naderi et al., 2020). FAO (2020) estimated the global potato production at about 90 million tons and reported that Iran has the third rank in the apple cultivation area and the sixth rank in its production rate (3,872,000 tons) after China, Poland, Italy, the US, and Chile. About 1.5 million tons of the apples produced in Iran are exported. Apples in Iran are cultivated in an area of 296,000 ha, out of which 12,000 ha are located in Tehran province, making it the top producer in this country (Mohammadian and Niknami, 2022). Apple production, marketing, and sales account for a significant part of the local people’s economy in Tehran province, especially in Damavand County (Igdir et al., 2015). Twenty types of high-quality commercial apples are harvested in this county, some with national and global fame (Godoun Malakeh, Arous, Barabaran, Jonagold, Fuji, Gala, Granny Smith, Two-Toned French, Red French, Golden Delicious, and Red Delicious), out of which about 80 percent are exported (Naderi et al., 2020).
The lack of an efficient brand in the apple production industry in Iran, especially in Damavand, is a key reason for its weak export. The export priorities have changed in the global market, so the sales market cannot be expanded by traditional trade practices (Sugianto et al., 2023). Since this has been neglected in Iran (Mehtari Arani et al., 2019), Damavand is losing its share in domestic and international markets (Azaron et al., 2021). This problem can be solved by raising awareness of the importance of brand building and promotion for exporting products, such as apples, and approaches used by top brands for survival in the global market. This research aims to analyze the effect of geographical brands on improving the efficiency of the Apple supply chain. The specific objectives included the study of the role of Apple’s brand image, attitude toward the brand, brand purchase intention, special brand value, brand personality, loyalty to the brand, brand experience, and brand identity in improving its supply chain efficiency. To achieve this goal, the following hypotheses were developed:
H1: The Apple brand image is effective in improving the efficiency of its supply chain.
H2: The attitude to the Apple brand is effective in improving the efficiency of its supply chain.
H3: The intention to purchase the Apple brand is effective in improving the efficiency of its supply chain.
H4: The Apple special brand value is effective in improving the efficiency of its supply chain.
H5: The Apple brand personality is effective in improving the efficiency of its supply chain.
H6: Royalty to the Apple brand is effective in improving the efficiency of its supply chain.
H7: The Apple brand experience is effective in improving the efficiency of its supply chain.
H8: The Apple brand identity is effective in improving the efficiency of its supply chain.
Accordingly, the conceptual model of the research was developed as depicted in Figure 1.
Figure 1
2 Methodology
From the perspective of research classification, this research is an applied study in terms of the goal. It also pursues effective measures to improve the efficiency of the Apple supply chain and tries to find the likely solutions for the problem. In terms of the possibility of variable control, it is quasi-experimental because the variables cannot be controlled. In addition, it is quantitative in terms of the methodology.
2.1 Study site
Damavand, the center of Damavand County, is located in Tehran province, 25 km from the southeast of Mount Damavand and 74 km from the east of Tehran City (Figure 2). Its elevation is 1960 m from the sea level, and it is fed by several rivers – one from the east originating from Lake Tar and another from the northwest originating from Mosha and Tizab, which join together to form the Damavand River. This city is located in the east of Tehran with a distance of 46 km from Iran’s Capital. Based on the climatic classification, it has a cold semi-steppe climate in the middle part and a mountainous climate in the elevations. Its maximum elevation from sea level is about 2000 meters, its temperature rises to a maximum of 35°C in the summer and falls to a minimum of −20°C in the winter, and its precipitation rate is 325 mm, but higher in the elevations. In the remote past, this county was an important agricultural and animal husbandry center, producing such products as apples, cherries, sour cherries, walnuts, potatoes, cucumbers, apricots, and other fruits and vegetables. Furthermore, it produced various animal products (Kheyroddin et al., 2017).
Figure 2
2.2 Statistical population and sampling method
The statistical population was composed of all apple orchard owners in Damavand County, amounting to 5,695 people. The sample size was estimated at 360 people by Cochran’s formula and was taken by simple randomization.
2.3 Research instrument
To achieve the research goal, a quantitative questionnaire was designed as the main research instrument, and the data were collected using the questionnaire with a review of the literature. Based on the conceptual framework of the research, the questionnaire was composed of 13 main sections scored on a five-point Likert scale (1 = very low; 2 = low; 3 = moderate; 4 = high; 5 = very high), as well as a section for collecting data on the participants’ demographic characteristics. The main sections included brand image (5 items), brand attitude (5 items), brand purchasing intention (5 items), special brand value (5 items), brand personality (5 items), brand loyalty (5 items), brand experience (5 items), brand identity (5 items), reliability (5 items), accountability (5 items), flexibility (5 items), cost (5 items), and asset (5 items). Table 1 presents the research criteria.
Table 1
| Criteria | Definition | References |
|---|---|---|
| Brand image | Brand perception is the mental linkage of a brand within the consumer’s memory. A customer conjures up an image based on all the signals emitted by the brand, such as its name, product appearance, advertising, and messaging. | Helmi et al. (2022) |
| Attitude to brand | An individual’s attitude, whether positive or negative, can influence their decision to adopt or reject a particular behavior. Essentially, the attitude towards using is the individual’s favorable or unfavorable sentiment regarding the enactment of that behavior. | Helmi et al. (2022) |
| Brand purchase intention | It significantly impacts the ultimate behavior of product usage. | Alalwan et al. (2018) |
| Special brand value | Special brand value signifies the worth of a specific brand in the consumer’s mind. Also, an array of assets and liabilities linked to a brand’s name and symbols, which in turn affect the product’s or service’s value to both the company and its customers, is known as the special brand value. | Sutanto and Kussudyarsana (2024), Alalwan et al. (2018) |
| Brand personality | It encapsulates the anthropomorphic attributes assigned to a brand, enabling consumers to form a profound emotional bond with it. | Alalwan et al. (2018) |
| Brand loyalty | It represents the consumer’s preference to consistently choose a specific brand over others, despite the presence of rational alternatives. | Parris and Guzman (2023) |
| Brand experience | It pertains to the consumer’s actual experience of a product or service, beyond mere perception, and the enhancement of this experience can sway the degree of consumer contentment and fidelity. | Jingcheng et al. (2023), Parris and Guzman (2023) |
| Brand identity | Brand identity is composed of the brand’s values, its communication style with the product, relation with audience, and essentially the same sentiment that the creator desires customers to feel during their engagement with the brand. | Jingcheng et al. (2023) |
| Reliability | It indicates the capability to execute tasks as anticipated. | Paramitha et al. (2023) |
| Responsiveness | It denotes promptness in accomplishing tasks. Responsiveness is an attribute focused on the customer and pertains to the time frame within which orders are fulfilled. | Paramitha et al. (2023), Nguyen et al. (2021) |
| Flexibility | Flexibility is the response to preplanned changes and demonstrates the capacity to react to external forces and the agility to modify accordingly. | Maaz and Ahmad (2022) |
| Cost | This characteristic encompasses the operational expenses of processes, including labor costs, transportation of raw materials, cost of goods sold, and expenses related to supply chain management. | Maaz and Ahmad (2022) |
| Asset | Strategies for asset management within a supply chain involve reducing inventory levels and optimizing internal operations, which are linked to the liquidity timeframe and the turnover of fixed assets. | Maaz and Ahmad (2022) |
The research criteria.
To check the questionnaire’s validity, it was supplied to a group of research committee professors (supervisor and advisor) and sales and marketing experts and managers. After applying the revisions required, it was ensured that the questions could be used for measurement. Based on the research content and properties, the questionnaire’s reliability was measured in a pilot study in which 30 questionnaires were distributed among farmers in Tehran outside the research realm to be filled out. After ensuring the validity (as confirmed by the research committee and sales and marketing experts and managers) and reliability of the questionnaire (Cronbach’s alpha in the range of 0.80–0.89) and making the required revisions, the final questionnaire was ready to use. It should be noted that some participants were not literate enough to fill out the questionnaire, so the researchers helped them in face-to-face meetings to resolve the possible ambiguities and provide explanations if required. So, the required data was collected. The validity and reliability of the final questionnaire were checked by three types of validity including content, convergent, and divergent validity, as well as reliability. The content validity, which was achieved through an opinion poll among professors, was checked to ensure the consistency of the measurement indicators and the existing literature (Hair et al., 2021). Convergent validity is based on the rationale that the indicators of each construct are correlated with one another. According to Fornell and Larcker (1981), convergent validity is said to be achieved if the average variance extracted (AVE) is greater than 0.5. Divergent validity is measured by comparing the root of AVE with the correlation of the hidden variables (Table 2). The root of AVE must be greater than the correlation of that construct with the other constructs of the research (Hair et al., 2021). The reliability in this research was checked by Cronbach’s alpha and the coefficient of composite reliability (Fornell and Larcker, 1981). Cronbach’s alpha was greater than the acceptable level (0.7) for all variables. Unlike Cronbach’s alpha, which implies that all indicators have similar weights, composite reliability relies on the real factor loadings of each construct, so it is a better criterion of reliability. The minimum acceptable level of composite reliability to prove the construct’s internal stability is 0.7 (Hair et al., 2021). Tables 2, 3 present the results about the reliability and validity of the research instrument.
Table 2
| Variables | Average variance extract (AVE) | Composite reliability (CR) | Cronbach’s alpha |
|---|---|---|---|
| Brand image | 0.61 | 0.84 | 0.83 |
| Brand attitude | 0.52 | 0.87 | 0.81 |
| Brand purchasing intention | 0.64 | 0.84 | 0.80 |
| Special brand value | 0.52 | 0.80 | 0.81 |
| Brand personality | 0.61 | 0.83 | 0.84 |
| Brand loyalty | 0.53 | 0.85 | 0.78 |
| Brand experience | 0.64 | 0.87 | 0.82 |
| Brand identity | 0.58 | 0.80 | 0.84 |
| Reliability | 0.72 | 0.92 | 0.90 |
| Accountability | 0.76 | 0.85 | 0.92 |
| Flexibility | 0.61 | 0.88 | 0.84 |
| Cost | 0.60 | 0.88 | 0.83 |
| Asset | 0.60 | 0.88 | 0.83 |
The convergent validity and the reliability of the measurement instrument.
Table 3
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1. Brand image | 0.762* | ||||||||||||
| 2. Brand attitude | 0.701 | 0.794* | |||||||||||
| 3. Brand purchasing intention | 0.272 | 0.651 | 0.801* | ||||||||||
| 4. Special brand value | 0.627 | 0.192 | 0.326 | 0.766* | |||||||||
| 5. Brand personality | 0.138 | 0.171 | 0.624 | 0.751 | 0.749* | ||||||||
| 6. Brand loyalty | 0.422 | 0.484 | 0.484 | 0.285 | 0.156 | 0.784* | |||||||
| 7. Brand experience | 0.160 | 0.172 | 0.086 | 0.562 | 0.270 | 0.415 | 0.852* | ||||||
| 8. Brand identity | 0.424 | 0.614 | 0.349 | 0.504 | 0.376 | 0.106 | 0.130 | 0.854* | |||||
| 9. Reliability | 0.484 | 0.351 | 0.154 | 0.264 | 0.312 | 0.624 | 0.670 | 0.754 | 0.884* | ||||
| 10. Accountability | 0.200 | 0.472 | 0.321 | 0.321 | 0.325 | 0.451 | 0.290 | 0.541 | 0.624 | 0.802* | |||
| 11. Flexibility | 0.470 | 0.268 | 0.378 | 0.261 | 0.418 | 0.440 | 0.641 | 0.641 | 0.254 | 0.780 | 0.841* | ||
| 12. Cost | 0.220 | 0.351 | 0.190 | 0.280 | 0.454 | 0.521 | 0.480 | 0.528 | 0.640 | 0.420 | 0.776 | 0.812* | |
| 13. Asset | 0.261 | 0.501 | 0.218 | 0.174 | 0.284 | 0.380 | 0.514 | 0.547 | 0.510 | 0.651 | 0.648 | 0.719 | 0.810 |
The correlation matrix and the study of divergent validity.
*p < 0.05.
According to the results and the output of the Smart PLS3 software suite in Table 2, it is evident that AVE, CR, and Cronbach’s alpha are all at suitable and acceptable levels, reflecting the proper validity and reliability of the measurement instrument.
Also, Table 3 presents that the constructs are fully independent. In other words, the values in the main diagonal (the square root of AVE) for each hidden variable are greater than the correlation of that variable with the other reflective hidden variables in the model. Thus, the research instrument is valid enough. The research data were analyzed at descriptive and inferential levels using the SPSS23 and Smart PLS3 software packages.
3 Results
The descriptive results showed that the participants were, on average, 43 years old. The education of most of them (37.45%) was at the diploma and associate degree levels. The majority of them (86.8) were male, while females constituted only 13.2% (Table 4).
Table 4
| Frequency | Percentage | |||
|---|---|---|---|---|
| Age | 20–30 years | 75 | 20 | Mean = 43 years SD = 9.58 Min = 20 years Max = 72 years |
| 31–40 years | 85 | 24 | ||
| 41–50 years | 110 | 30 | ||
| 51–60 years | 54 | 15 | ||
| >61 years | 36 | 11 | ||
| Total | 360 | 100 | ||
| Educational level | Illiterate | 36 | 10 | Mode: bachelor’s degree |
| Under diploma | 78 | 21.6 | ||
| Diploma and associate degree | 118 | 32.7 | ||
| Bachelor’s degree | 92 | 25.5 | ||
| Master’s degree | 30 | 8.3 | ||
| Ph.D. | 6 | 1.9 | ||
| Total | 360 | 100 | ||
| Gender | Female | 48 | 13.2 | Mode: male |
| Male | 312 | 86.8 | ||
| Total | 360 | 100 |
The frequency distribution of the participants based on age, gender, and educational level.
In the next step, the relationships of the variables were studied by the structural model using the PLS method, in which the T-values and the standardized estimation were included in both modes. First, to prove the research hypotheses, the Bootstrapping command was used in Smart PLS, which shows the output of t-values (Figure 3). As is seen in Figure 4, the t-values were greater than 1.96 for all main constructs, except for brand personality, which confirms the research hypotheses.
Figure 3
Figure 4
Table 5 presents the results of implementing the model in two modes of T-values and standardized estimation.
Table 5
| Variables | Items | Factor loadings | T-value | |
|---|---|---|---|---|
| X1 | Brand image |
| 0.737 | 23.84 |
| X2 |
| 0.804 | 19.15 | |
| X3 |
| 0.754 | 24.54 | |
| X4 |
| 0.815 | 36.64 | |
| X5 |
| 0.754 | 29.03 | |
| X6 | Brand attitude |
| 0.846 | 28.44 |
| X7 |
| 0.794 | 24.69 | |
| X8 |
| 0.845 | 26.54 | |
| X9 |
| 0.837 | 23.09 | |
| X10 |
| 0.802 | 22.08 | |
| X11 | Brand purchasing intention |
| 0.865 | 49.58 |
| X12 |
| 0.819 | 35.61 | |
| X13 |
| 0.895 | 24.44 | |
| X14 |
| 0.865 | 20.54 | |
| X15 |
| 0.827 | 19.14 | |
| X16 | Special brand value |
| 0.832 | 30.25 |
| X17 |
| 0.811 | 22.46 | |
| X18 |
| 0.723 | 23.54 | |
| X19 |
| 0.816 | 44.64 | |
| X20 |
| 0.895 | 40.25 | |
| X21 | Brand personality |
| 0.812 | 56.15 |
| X22 |
| 0.796 | 40.52 | |
| X23 |
| 0.781 | 56.83 | |
| X24 |
| 0.908 | 32.14 | |
| X25 |
| 0.835 | 42.42 | |
| X26 | Brand loyalty |
| 0.891 | 41.19 |
| X27 |
| 0.827 | 36.54 | |
| X28 |
| 0.842 | 39.46 | |
| X29 |
| 0.826 | 31.15 | |
| X30 | Brand experience |
| 0.851 | 28.96 |
| X31 |
| 0.894 | 19.56 | |
| X32 |
| 0.812 | 41.54 | |
| X33 |
| 0.862 | 23.54 | |
| X34 |
| 0.863 | 39.60 | |
| X35 |
| 0.799 | 45.58 | |
| X36 | Brand identity |
| 0.839 | 43.15 |
| X37 |
| 0.821 | 39.58 | |
| X38 |
| 0.872 | 40.15 | |
| X39 |
| 0.881 | 35.26 | |
| X40 |
| 0.842 | 31.42 | |
| X41 | Reliability |
| 0.762 | 30.65 |
| X42 |
| 0.729 | 26.72 | |
| X43 |
| 0.831 | 29.48 | |
| X44 |
| 0.839 | 32.48 | |
| X45 |
| 0.892 | 24.94 | |
| X46 | Accountability |
| 0.894 | 23.62 |
| X47 |
| 0.812 | 22.08 | |
| X48 |
| 0.832 | 33.25 | |
| X49 |
| 0.843 | 31.95 | |
| X50 |
| 0.894 | 21.18 | |
| X51 | Flexibility |
| 0.809 | 23.48 |
| X52 |
| 0.814 | 45.53 | |
| X53 |
| 0.862 | 24.98 | |
| X54 |
| 0.901 | 35.74 | |
| X55 |
| 0.849 | 35.54 | |
| X56 | Cost |
| 0.861 | 31.58 |
| X57 |
| 0.865 | 30.14 | |
| X58 |
| 0.813 | 28.01 | |
| X59 |
| 0.912 | 26.83 | |
| X60 |
| 0.882 | 27.51 | |
| X61 | Asset |
| 0.841 | 30.62 |
| X62 |
| 0.837 | 33.65 | |
| X63 |
| 0.831 | 29.56 | |
| X64 |
| 0.849 | 25.10 | |
| X65 |
| 0.769 | 29.69 |
The items, factor loadings, and T-values for the research variables.
The fitness of a structural model is checked by calculating R2 for the endogenous hidden (dependent) variables. The three values of 0.19, 0.33, and 0.67 show the thresholds of weak, moderate, and strong R2, respectively. It was strong in this research, as is seen in Table 4. Also, Q2 represents the model’s predictivity. The Q2 values of 0.02, 0.15, and 0.35 show that the target endogenous construct is weakly, moderately, and strongly capable of predicting the related exogenous construct or constructs, respectively (Hair et al., 2021). Table 6 proves the proper predictivity of the research model and confirms the suitable fitness of the structural model.
Table 6
| Hidden variables | R2 | Q2 |
|---|---|---|
| Improving supply chain efficiency | 0.858 | 0.450 |
The results of R2 and Q2 for endogenous construct.
Finally, the general fitness of the model was determined by GOF, calculated by the following equation:
Wetzels et al. (2009) suggested 0.01, 0.25, and 0.36 as the thresholds for weak, moderate, and strong GOF, respectively. It was estimated at 0.812 in the present work, reflecting the strong fitness of the research model.
The fitness of the model was evaluated by aggregating its fit indices. Once judged, hypotheses were either supported or refuted using t-values. To test the hypotheses, the t-value was compared with +1.96 and − 1.96. If it fell within this range, the hypothesis was refuted; otherwise, it was supported. Table 5 displays the results of the hypotheses testing.
The analysis of the results revealed that among the components underpinning the improvement of the supply chain efficiency, special brand value (β = 0.541; T-value = 5.854; p < 0.01) was the most effective. The subsequent ranks were for the components of brand loyalty (β = 0.484; T-value = 5.019), brand image (β = 0.545; T-value = 4.794), brand attitude (β = 0.438; T-value = 4.612), brand experience (β = 0.398; T-value = 4.168), brand purchasing intention (β = 0.327; T-value = 3.921), and brand identity (β = 0.291; T-value = 3.495), respectively (Table 7).
Table 7
| Original sample (O) | Sample mean (M) | Standard deviation (STDEV) | T-statistic (|O/STDEV|) | p-values | |
|---|---|---|---|---|---|
| 0.541 | 0.691 | 0.084 | 5.854 | 0.000 |
| 0.484 | 0.477 | 0.13 | 5.019 | 0.000 |
| 0.454 | 0.155 | 0.078 | 4.794 | 0.037 |
| 0.438 | 0.389 | 0.095 | 4.612 | 0.000 |
| 0.327 | 0.367 | 0.112 | 3.921 | 0.001 |
| 0.291 | 0.391 | 0.077 | 3.495 | 0.000 |
| 0.134 | 0.209 | 0.074 | 1.081 | 0.218 |
The linear effect of the research variables for testing the research hypotheses.
4 Discussion and conclusions
Marketing experts and scholars refer to the future of marketing as “the world of brand management and branding activities.” A reputable brand name serves as a reliable source of information for customers who are considering purchasing a product. This reduces the risk and eventually eases the decision-making process. The research findings indicate that the variables used in the study provided a good framework for analyzing the impact of geographical (location-based) brands on enhancing the efficiency of the Apple supply chain in Iran.
As was revealed by the results, the specific brand value is effective in improving the Apple supply chain. This means that brands with higher specific values tend to have a more favorable image among customers, giving them an edge over their competitors. This will increase sales and further develop the market for the apple product. This conclusion is consistent with the findings of reports; by (Lin et al., 2021, and Le-Hoang et al., 2020).
Brand loyalty is an important factor in the efficiency of the Apple supply chain, from the perspective of the participants. Loyal customers in Iran have a clear image of the brand due to effective marketing. They recommend the brand to others, which leads to increased sales and market development. Additionally, customer loyalty helps to retain previous customers and attract new ones. Loyal customers motivate producers and retailers to improve supply chain management to increase customer trust, which will happen through an efficient supply chain. These findings are corroborated by the results of (Lin et al., 2019; Hu et al., 2021; Tran et al., 2023).
The participants suggested the effectiveness of brand image in improving the Apple supply chain. It is crucial to create and reinforce the mental image of the brand to retain loyal customers. Brand image is the perception of the quality associated with the brand name by the customers. Many apple farmers can promote their brand image among the customers to enhance their loyalty and attract more loyal customers, ultimately leading to more profits. Similar results have been reported in (Zameer et al., 2020; Mitchell and Balabanis, 2021).
According to the participants, brand attitude can improve the Apple supply chain. People’s attitudes tend to influence their behaviors towards different things, leading to various outcomes. In the meantime, there is a rather simple and set-in-stone principle: the higher the profit gained from purchasing or using a product/service, the more positive the attitude toward it becomes and the higher the willingness of the consumers to continue using it. This entails the consumers’ satisfaction with the product/service and their loyalty to its supplier. This finding agrees with the results of (Rezaei et al., 2020; Chatterjee et al., 2021; Chuenban et al., 2021).
According to the participants, the brand experience can improve the Apple supply chain. When customers have a satisfying experience consuming a particular brand of agricultural products, e.g., Apples, which are directly related to their health, they are likely to re-purchase it because the brand has been successful in satisfying their requirements. This, in turn, shows the customers’ loyalty to the brand since the brand holds a significant place among customers, and they will keep supporting it. This result is consistent with those reported in (Wang et al., 2022; Niknami, 2024).
The studied people expressed that the intention of customers to purchase a particular brand plays a significant role in improving the Apple supply chain. It means that when customers intend to purchase a product, they help the promotion of its brand and recommend it to others, too. Recommending the purchase of a brand is a sort of promotion. This increases its sales and the retailer’s profit. Consequently, the retailer will decide to develop the product, thereby improving its supply chain. The intention to purchase products is directly related to the development of their market domestically and abroad. When more consumers show interest in a product, its market expands, and the need for support also increases. The need for support, in turn, improves the efficiency of the supply chain, enhances consumers’ satisfaction, and increases their intention to purchase. By strengthening the supply chain, the chain of purchase, consumption, and satisfaction is completed and repeated. This result is consistent with the reports of (Le-Hoang et al., 2020; Chi, 2021; Wang et al., 2022).
It was found that brand identity can effectively improve the Apple supply chain. Brand identity is a set of visual components, e.g., logos, colors, and details, which represent the ideas of a business and allow customers to memorize a brand and distinguish it from others. Since brand identity tries to improve product design and appearance, it is effective in improving product marketing and marketability. Improving product design needs focusing on research and development and collecting customer feedback about the product, which finally leads to developing the supply chain. Regarding the apple product, it can be said that producing and categorizing apples in different sizes can influence their marketability. On the other hand, by promoting the geographical brand, we also aim to provide the Apple product with an identity in the international dimension. Thus, to be famous at the national and international levels, a brand must have an efficient and stable supply chain to respond to customer needs at different levels. Similar results have been reported in (Uzelac et al., 2022; Vi et al., 2023).
Brand personality does not affect improving the Apple supply chain according to the participants. Brand personality refers to the set of characteristics that form the foundation of a business or organization and influence people’s perceptions of its products, services, goals, and values. Brand personality can refer to its long-term goals in the market by providing emotional, psychological, or physical responses. It can, however, be understood that in the case of apples produced in Damavand, the use of different trademarks and improper branding have rendered brand personality irrelevant to customers. As a result, customers only consider the appearance and quality of the product and do not respond to brand names. Also, branding has been improper in this field, so the product has been supplied to the market with no brand and under a general name, leading customers to become indifferent to brand names. Similar results have been found by (Tran et al., 2023; Vi et al., 2023).
The following recommendations can be put forth based on the results: supplying apple product in unique packages to tourists and visitors to create a brand identity and proper brand value for Damavand apple at different levels; supplying high-quality product to purchasers to foster reliability in its quality; developing storage facilities to supply it in all seasons; publishing extension magazines and planning to fulfill programs tailored to apple growers’ knowledge in Damavand County’s local media; improving the efficiency of Damavand apple supply chain by supplying high-quality product on a timely basis and at a proper price to foster a good attitude among consumers about Damavand apple brand; and providing high-quality apple in the region to tourists to create brand experience and increasing purchase intention at the national level; advertising various products to increase consumers’ purchase intention in the national and international markets; forming extension committees to train branding at the province level and planning for developing apple branding in the region, country, and world.
The results of the research have some theoretical and practical applications. A theoretical application is to study and compare successful patterns of geographical branding of crops in the world and the secret of their success. It can be concluded at the practical level that the variables used in the research were a suitable model to analyze the effect of geographical brand on improving the apple supply chain efficiency in Iran. The researchers tried to suggest a new approach to improving the supply chain based on the causal relations of the variables, in addition to studying the promotion of a geographical brand and marketing of Apple in the previous studies.
Like other research studies, this research is not without limitations, and addressing these limitations can pave the way for future quantitative and qualitative studies. First, the statistical population was limited to apple orchard owners in Damavand. While the findings provide valuable insights for improving the efficiency of the apple supply chain, they may not apply to other agricultural products. The research results can be interpreted within the framework of the proposed model. The cross-sectional nature of the research, inability to fully control all Unwanted variables and non-generalizability of the results of this study to other areas were its other limitations.
Statements
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the [patients/ participants OR patients/participants legal guardian/next of kin] was not required to participate in this study in accordance with the national legislation and the institutional requirements.
Author contributions
SN: Conceptualization, Data curation, Formal analysis, Investigation, Software, Visualization, Writing – original draft. MN: Conceptualization, Investigation, Methodology, Resources, Supervision, Validation, Writing – review & editing. MS: Conceptualization, Methodology, Resources, Validation, Writing – review & editing. HR: Investigation, Methodology, Resources, Validation, Visualization, Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Acknowledgments
The authors feel it necessary to express their gratitude to all those who helped in different stage of this research.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1
AlalwanA. A.BaabdullahA. M.RanaN. P.TamilmaniK.DwivediY. K. (2018). Examining adoption of mobile internet in Saudi Arabia: extending TAM with perceived enjoyment, innovativeness and trust. Technol. Soc.55, 100–110. doi: 10.1016/j.techsoc.2018.06.007
2
Ameri SiyahooeiM.LarisemnaniB.Mahmoudi MaymandM.ParhizgarM. M. (2023). Compilation and explanation of the leadership brand model in the agricultural industry, the citrus product of the south of the country. Int. J. Nonlinear Anal. Appl.14, 2985–2998. doi: 10.22075/IJNAA.2022.28214.3837
3
AzaronA.VenkatadriU.Farhang DoostA. (2021). Designing profitable and responsive supply chains under uncertainty. Int. J. Prod. Res.59, 213–225. doi: 10.1080/00207543.2020.1785036
4
ChatterjeeS.RanaN. P.TamilmaniK.SharmaA. (2021). The effect of AI-based CRM on organization performance and competitive advantage: an empirical analysis in the B2B context. Ind. Mark. Manag.97, 205–219. doi: 10.1016/j.indmarman.2021.07.013
5
ChenB.ZhangM.ChenH.MujumdarA. S.GuoZ. (2023). Progress in smart labels for rapid quality detection of fruit and vegetables: a review. Postharvest Biol. Technol.198:112261. doi: 10.1016/j.postharvbio.2023.112261
6
ChiN. T. K. (2021). Understanding the effects of eco-label, eco-brand, and social media on green consumption intention in ecotourism destinations. J. Clean. Prod.321:128995. doi: 10.1016/j.jclepro.2021.128995
7
ChoupannejadR.SharifnabiB.BaharM.TalebiM. (2018). Searching for resistance genes to Venturia in aequalis in wild and domestic apples in Iran. Sci. Horticult.232, 107–111. doi: 10.1016/j.scienta.2018.01.006
8
ChuenbanP.SornsaruhtP.PimdeeP. (2021). How brand attitude, brand quality, and brand value affect Thai canned tuna consumer brand loyalty. Heliyon7:e06301. doi: 10.1016/j.heliyon.2021.e06301
9
DissanayakeC. K.CrossJ. A. (2018). Systematic mechanism for identifying the relative impact of supply chain performance areas on the overall supply chain performance using SCOR model and SEM. Int. J. Prod. Econ.201, 102–115. doi: 10.1016/j.ijpe.2018.04.027
10
EscribanoM.GasparP.MesiasF. J. (2020). Creating market opportunities in rural areas through the development of a brand that conveys sustainable and environmental values. J. Rural. Stud.75, 206–215. doi: 10.1016/j.jrurstud.2020.02.002
11
EssaK.HassenY. (2022). Challenges and opportunities of branding agricultural commodities: the case of Rice production in South Gondar zone, Ethiopia. NeuroQuantology20, 4063–4075. doi: 10.48047/nq.2022.20.13.NQ88494
12
FAO. (2020). Agriculture and consumer protection: detail. Available at: http://www.fao.org/home/search/en/?q=Area.under.apple
13
FornellC.LarckerD. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res.18, 39–50. doi: 10.1177/002224378101800104
14
ForoudiP. (2019). Influence of brand signature, brand awareness, brand attitude, brand reputation on hotel industry’s brand performance. Int. J. Hosp. Manag.76, 271–285. doi: 10.1016/j.ijhm.2018.05.016
15
GardnerT. A.BenzieM.BörnerJ.DawkinsE.FickS.GarrettR.et al. (2019). Transparency and sustainability in global commodity supply chains. World Dev.121, 163–177. doi: 10.1016/j.worlddev.2018.05.025
16
GhasemiM.NiknamiM.RafieeH. (2019). Factors affecting knowledge and attitude of farmers toward relative advantage of crops in Garmsar County. Iran. J. Agric. Econ. Dev. Res.50, 677–690. doi: 10.22059/IJAEDR.2019.274619.668707
17
GhazinooriS.OlfatL.SoofiJ. B.AhadiR. (2020). Investigating the organic agricultural products supply chain in Iran. Int. J. Agric. Manag. Dev.10, 71–85. doi: 10.22004/ag.econ.335109
18
GuoY.YinY. (2022). Analysis on optimization of agricultural products supply chain based on dynamic system. Discret. Dyn. Nat. Soc.2022, 1–13. doi: 10.1155/2022/1656636
19
HairJ.HairJ. F.HultG. T. M.RingleC. M.SarstedtM. (2021). A primer on partial least squares structural equation modeling (PLS-SEM): Sage publications.
20
HelmiS.ArianaS.SupardinL. (2022). The role of brand image as a mediation of the effect of advertising and sales promotion on customer purchase decision. J. Econ. Sustain. Dev.13, 90–99. doi: 10.7176/JESD/13-8-09
21
HuN.ChenX.ZhangN. (2021). Influence of service quality of agricultural products e-commerce platform on customer loyalty-the mediating role of customer engagement. Int. J. Smart Bus. Technol.9, 13–28. doi: 10.21742/IJSBT.2021.9.1.02
22
IgdirH. B.MohammadrezaeiR.ZariffianS. (2015). Investigating the role of exporting skills of apple exporters in apple export development (case study: West Azerbaijan). J. Int. Food Agribus. Mark.27, 273–289. doi: 10.1080/08974438.2014.918918
23
JafariH.GhaderiH.MalikM.BernardesE. (2023). The effects of supply chain flexibility on customer responsiveness: the moderating role of innovation orientation. Prod. Plan. Control34, 1543–1561. doi: 10.1080/09537287.2022.2028030
24
JingchengS.RaoY.JingH.YatingX.IdrisM. Z. (2023). Brand equity and brand personality: a literature review and future research directions. Int. J. Acad. Res. Econ. Manag. Sci.12:4. doi: 10.6007/IJAREMS/v12-i4/20092
25
JungJ.MaedaM.ChangA.BhandariM.AshapureA.Landivar-BowlesJ. (2021). The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems. Curr. Opin. Biotechnol.70, 15–22. doi: 10.1016/j.copbio.2020.09.003
26
KheyroddinR.PirooziR.SoleimaniA. (2017). Metastatic spread of luxury second homes in rural areas: a new type of spatial development in the Tehran metropolitan region: a study of Damavand county, Iran. J. Arch. Plan. Res.34, 71–88.
27
Le-HoangP. V.NguyenG. T.PhungH. T. T.HoV. T.PhanN. T. (2020). The relationship between brand equity and intention to buy: the case of convenience stores. Independ. J. Manag. Product.11, 434–449. doi: 10.14807/ijmp.v11i2.1062
28
LiG.WuH.SethiS. P.ZhangX. (2021). Contracting green product supply chains considering marketing efforts in the circular economy era. Int. J. Prod. Econ.234:108041. doi: 10.1016/j.ijpe.2021.108041
29
LiaoM.ZhangJ.WangR.QiL. (2021). Simulation research on online marketing strategies of branded agricultural products based on the difference in opinion leader attitudes. Inform. Process. Agric.8, 528–536. doi: 10.1016/j.inpa.2020.12.001
30
Lima-JuniorF. R.CarpinettiL. C. R. (2020). An adaptive network-based fuzzy inference system to supply chain performance evaluation based on SCOR® metrics. Comput. Ind. Eng.139:106191. doi: 10.1016/j.cie.2019.106191
31
LinW. L.HoJ. A.SambasivanM.YipN.MohamedA. B. (2021). Influence of green innovation strategy on brand value: the role of marketing capability and R&D intensity. Technol. Forecast. Soc. Chang.171:120946. doi: 10.1016/j.techfore.2021.120946
32
LinC.-W.WangK.-Y.ChangS.-H.LinJ.-A. (2019). Investigating the development of brand loyalty in brand communities from a positive psychology perspective. J. Bus. Res.99, 446–455. doi: 10.1016/j.jbusres.2017.08.033
33
LiuY.WangX. (2022). Promoting the competitiveness of green brands of agricultural products based on agricultural industry clusters. Wirel. Commun. Mob. Comput.2022, 1–18. doi: 10.1155/2022/7824638
34
López-BayónS.González-DíazM.Solís-RodríguezV.Fernández-BarcalaM. (2018). Governance decisions in the supply chain and quality performance: the synergistic effect of geographical indications and ownership structure. Int. J. Prod. Econ.197, 1–12. doi: 10.1016/j.ijpe.2017.12.022
35
MaazM. A. M.AhmadR. (2022). Impact of supply chain performance on organizational performance mediated by customer satisfaction: a study of the dairy industry. Bus. Process. Manag. J.28, 1–22. doi: 10.1108/BPMJ-05-2021-0292
36
MaghrebiM.NooriR.BhattaraiR.Mundher YaseenZ.TangQ.Al-AnsariN.et al. (2020). Iran’s agriculture in the Anthropocene. Earth’s Future8:e2020EF001547. doi: 10.1029/2020EF001547
37
Mehtari AraniM.Baghbani AraniA.Maghsoudi GanjehY.AbdolmanafiS. (2019). Examining and ranking factors affecting the branding of agricultural products in Isfahan’s rural cooperative companies (case study: potato production). Rural Dev. Strat.6, 319–332. doi: 10.22048/rdsj.2020.201369.1816
38
MitchellV. W.BalabanisG. (2021). The role of brand strength, type, image and product-category fit in retail brand collaborations. J. Retail. Consum. Serv.60:102445. doi: 10.1016/j.jretconser.2021.102445
39
ModakN. M.SenapatiT.SimicV.PamucarD.SahaA.Cárdenas-BarrónL. E. (2024). Managing a sustainable dual-channel supply chain for fresh agricultural products using blockchain technology. Expert Syst. Appl.244:122929. doi: 10.1016/j.eswa.2023.122929
40
MohammadianM.NiknamiM. (2022). Bridging the knowledge gap of apple growers: transition from conventional to organic production pattern in Iran. Europe21, 22–29. doi: 10.30682/nm2202d
41
MuJ.LiJ.LiY.LiuC. (2021). The dynamics of brand-driven quality improvement decision-making in multi-small-supplier Agri-food supply chain: the case of China. Sustain. For.13:10815. doi: 10.3390/su131910815
42
NaderiS.RainiG. N.TakiM. (2020). Measuring the energy and environmental indices for apple (production and storage) by life cycle assessment case study: Semirom county, Isfahan, Iran. Environ. Sustain. Indic.6:100034. doi: 10.1016/j.indic.2020.100034
43
NayeemT.MurshedF.DwivediA. (2019). Brand experience and brand attitude: examining a credibility-based mechanism. Mark. Intell. Plan.37, 821–836. doi: 10.1108/MIP-11-2018-0544
44
NguyenH.PhamV. K.PhanT. T. (2021). Determinants of export organic supply chain performance: an empirical study of fruits and vegetables in Vietnam. J. Int. Logist. Trade19, 147–161. doi: 10.24006/jilt.2021.19.3.147
45
NiknamiM. (2024). An Analysis of Customer-based Brand Equity in the Field of Agricultural Extension and Education: Multi-Criteria Decision-Making Approach. Agricultural Extension and Education Research, 1, 21–44.
46
NiyasN.KavidaV. (2022). Impact of financial brand values on firm profitability and firm value of Indian FMCG companies. IIMB Manag. Rev.34, 346–363. doi: 10.1016/j.iimb.2023.01.001
47
NooriS.NiknamiM.SabouriM. S. (2024). Analyzing the branding challenges of Damavand’s apple: using grounded theory. Iran. J. Agric. Econ. Dev. Res.55, 113–129. doi: 10.22059/IJAEDR.2023.352345.669195
48
ParamithaA. W. L.SantosaW.TriwulandariS. D. (2023). The effect of supply chain responsiveness, flexibility, & quality on customer development. J. Int. Trade Logist. Law9, 77–87.
49
ParrisD. L.GuzmanF. (2023). Evolving brand boundaries and expectations: looking back on brand equity, brand loyalty, and brand image research to move forward. J. Prod. Brand Manag.32, 191–234. doi: 10.1108/JPBM-06-2021-3528
50
PashovaS.RadevR. (2021). Labeling of fresh fruits and vegetables. Qual. Access Success22:181.
51
RayA.BalaP. K.ChakrabortyS.DasguptaS. A. (2021). Exploring the impact of different factors on brand equity and intention to take up online courses from e-learning platforms. J. Retail. Consum. Serv.59:102351. doi: 10.1016/j.jretconser.2020.102351
52
RezaeiR.SafaL.GanjkhanlooM. M. (2020). Understanding farmers’ ecological conservation behavior regarding the use of integrated pest management- an application of the technology acceptance model. Glob. Ecol. Conserv.22:e00941. doi: 10.1016/j.gecco.2020.e00941
53
SugiantoI. M.PujawanI. N.PurnomoJ. D. T. (2023). A study of the Indonesian trucking business: survival framework for land transport during the Covid-19 pandemic. Int. J. Disaster Risk Reduct.84:103451. doi: 10.1016/j.ijdrr.2022.103451
54
SutantoW. D. R.KussudyarsanaK. (2024). The role of Brand Trust, brand image, brand equity on repurchase intention. J. Ilmiah Manag. Kesatuan12, 119–128. doi: 10.37641/jimkes.v12i1.2395
55
TranT. V. T.HoQ. N.NguyenN. T.LeT.-P.NguyenH. A. D. (2023). Investigation factors of brand personality affecting purchase intentions towards authentic agricultural products in Vietnam. Int. J. Anal. Appl.21:70. doi: 10.28924/2291-8639-21-2023-70
56
TürkeșM. C.BănacuC.-S.StoenicăL. (2024). The effect of supply chain sustainability practices on Romanian SME performance. Sustain. For.16:2887. doi: 10.3390/su16072887
57
UzelacO.MijatovićM. D.LukinovićM. (2022). The role of branding agricultural products in better market valorization. Econ. Agric.69, 613–625. doi: 10.5937/ekoPolj2202613U
58
ViT. T. T.QuangH. N.AnhN. D. H.NamV. H. (2023). Brand authenticity and social identity theory as drivers of purchase intention towards the sustainable development of Vietnamese weasel coffee with the mediating role of Vietnamese law context. J. Law Sustain. Dev.11:e1541. doi: 10.55908/sdgs.v11i8.1541
59
WangE.LiuZ.GaoZ.WenQ.GengX. (2022). Consumer preferences for agricultural product brands in an E-commerce environment. Agribusiness38, 312–327. doi: 10.1002/agr.21732
60
WetzelsM.Odekerken-SchröderG.Van OppenC. (2009). Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Q.33, 177–195. doi: 10.2307/20650284
61
YarmandS.MohammadiH.KarbasiA.DehghaniM. (2023). The effect of the marketing mix and specialized knowledge on the export performance of SMEs exporting dry fruits. Journal of Agricultural Economics and Development37, 145–156. doi: 10.22067/jead.2023.80703.1176
62
ZameerH.WangY.YasmeenH. (2020). Reinforcing green competitive advantage through green production, creativity, and green brand image: implications for cleaner production in China. J. Clean. Prod.247:119119. doi: 10.1016/j.jclepro.2019.119119
63
ZhengX.HuangQ.ZhengS. (2022). The identification and applicability of regional brand-driving modes for agricultural products. Agriculture12:1127. doi: 10.3390/agriculture12081127
Summary
Keywords
apple, brand equity, brand, structural analysis, supply chain improvement
Citation
Noori S, Niknami M, Sabouri MS and Rafiee H (2024) How does geographical branding improve the efficiency of the apple supply chain?. Front. Sustain. Food Syst. 8:1410737. doi: 10.3389/fsufs.2024.1410737
Received
01 April 2024
Accepted
19 June 2024
Published
28 June 2024
Volume
8 - 2024
Edited by
Naser Valizadeh, Shiraz University, Iran
Reviewed by
Khadijeh Bazrafkan, Agricultural Research, Education and Extension Organization (AREEO), Iran
Negin Fallah Haghighi, Iranian Research Organization for Science and Technology, Iran
Latif Haji, Shiraz University, Iran
Updates
Copyright
© 2024 Noori, Niknami, Sabouri and Rafiee.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Mehrdad Niknami, m.niknami@iau-garmsar.ac.ir
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.