Value-Adding Practices in Food Supply Chain: Evidence from Indian Food Industry Shashi School of Management Studies, Punjabi University, Patiala, Punjab, India. E-mail:
[email protected] Rajwinder Singh International Management Institute, Bhubaneswar, Odisha, India. E-mail:
[email protected] Amir Shabani Department of Information, Logistics and Innovation, Faculty of Economics and Business Administration, VU University Amsterdam, Amsterdam, The Netherlands. E-mail:
[email protected];
[email protected] ABSTRACT
The study aims an attempt to realize the importance of value addition at different stages of food supply chain to see what the value addition practices mean across the food chain. To do so, this paper investigates the value addition relationships of different supply chain players from farm to retail level. Based on extensive literature review and deep discussions with supply chain practitioners as well as academicians, a conceptual framework is developed to assist those players in identifying the importance of adding value, defining a common definition of value addition practices, and getting motivation for superior value addition improvement. Value addition practices of five stages at the food supply chain, namely, farmer, supplier, processor, distributor and retailer, are conceptualized and formulated to tests the relationship among these stages. The study mainly focuses on the value addition of farm products in Indian scenario. The findings affirmed that the farmer’s value addition is positively related to supplier’s value addition, processor’s value addition, and distributor’s value addition. Moreover, supplier’s value addition is positively related to processor’s value addition, and processor’s value addition is positively related to distributor’s value addition. Besides, distributor’s value addition is positively related to retailer’s value addition. C 2016 Wiley Periodicals, Inc. [JEL Classification: M210].
1. INTRODUCTION
The Indian food sector has poised rapid growth and structural transformation that increase its share in the global food business every year. The trend of substantial public and private investments has taken significant heed with the intention of increasing production, procurement, processing, distribution, and retail competence. Moreover, the farmers have started adopting latest technologies in order to improve their efficiency, production, and profits (IIM, 2013). As a result, India has become third world leader in large-scale farm production. The multiple factors have enriched this growth of farm industry such as a rise in population, rise in household income, shift in consumption pattern, research and development, and expansion and adoption of technology in food processing units. Currently, two key ongoing trends within this industry are organic farming and adoption of information technology (IBEF, 2015). Generally, farm food supply chain (SC) is classified into two parts; “fresh unprocessed farm products” (e.g., vegetables and fruits) and “processed farm products” (e.g., convenience food and soft drinks). The perishable farm products such as food, dairy, meat, vegetables, mushrooms, flowers, and fruits, etc., get spoiled in a few days after production. Therefore, in order to attain more benefits from farm industry, India needs to work on two specific areas; infrastructure and postharvest wastage control. According to Conway-Gomez et al. (2010), the high population growth rate decreases the resource availability and it builds the pressure on food SC performance. The world population is estimated to be 9.3 billion by the end of 2050. At present, Agribusiness, Vol. 33 (1) 116–130 (2017) Published online in Wiley Online Library (wileyonlinelibrary.com/journal/agr).
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2016 Wiley Periodicals, Inc. DOI: 10.1002/agr.21478
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India is the second largest populated country next to China. But China’s population is anticipated to be lower than the populations of India by 2040 (USDA, 2014), which is creating a massive strain on Indian food SC to feed more than a billion Indian people (FICCI, 2014). In spite of large food production in India, the food industry is incapable to fulfill the basic requirements of Indian consumers like having affordable, sufficient and healthy as well as value-added food. The processing rate is only 2 to 3% of the total production that is quite less as compared to other dominant players of the world. Presently, India is facing the problems of food inflation and food security (Devi, 2014). Thus, the rate of postharvest farm losses in India is one of the hottest topics of discussion among SC academics and practitioners which is accounted up to 25–40% (Yadav, 2013). It shows the inconsistency of Indian food SC to store and distribute the perishable farm production. Hence, the need is not only to increase the production rate but also to confine the rate of wastage through adequate “food processing, cold chain, and more value addition (VA) at each stage.” In long turn, it can help the country to become a world food basket. Globalization and increased complexities in food trade have arisen the requirement for effective control over the foodstuffs to protect consumers from toxic, contaminated, and fraudulently presented food (Jandric, Islam, Singh, & Cannavan, 2015). The term VA was first introduced by Porter (1985) between 1970 and 1980s, which indicates an increase in the customer’s worth. Krishnapriya and Baral (2014) inferred that firms need to integrate their key business processes to deliver value-added products or services. It plays a vital role in building brand value and increasing customer crowd. Literature offers many investigations which provides relevant evidences related to the VA importance (for instance, see Anderson & Hanselka, 2013; Aworh, 2015; Chiang, 2010; Matthews, 2013; Murthy & Naidu, 2012). The high level of farm VA is actually important in India. However, the research on VA, especially at the food supply level, has not received good heed throughout the world. Few national and international government organizations such as National Bank For Agriculture and Rural Development, Canadian Food Inspection Agency, and United States Department of Agriculture have started highlighting this issue by providing the grants to the farm practitioners but a lot of efforts are yet needed (CFIA, 2010; Gruber & Panasiak, 2011; The Economic Survey 2014–15; Zhang, 2012). The high population growth rate, decrease in farming land, arisen of new health problems, rapid changes in customer’s value preferences, and increasing wastage rate are some of the biggest challenges in the front of the Indian food industry (Dev, 2008). To overcome the mentioned challenges, the identification of potential benefits of VA may be an important subject (Devi, 2014). Hence, the companies need to understand the VA practices that help in tackling these challenges and delight the customers. Therefore, the objective of this paper is to investigate the relationship between different parties involved in the food SC to improve overall as well as individual benefits of the SC players. The rest of this paper is organized as follows. Section 2 reviews the literature on food supply VA and its importance. Then, a conceptual framework for food VA during SC is proposed in Section 3. Research methodology is discussed by Section 4, and Section 5 highlighted the statistical analysis. Finally, Section 6 reveals study findings and concluding remarks along with some future research directions.
2. LITERATURE REVIEW
In this section, we reviewed the previous studies available on the farm supply VA for the sake of identifying the available gap between theory and practices, and thereafter, formulate a conceptual framework for different farm supply stage VA. According to Liu and Lian (2009), VA is a systematic integration of SC processes in which the worth of the products starts increasing from production point and becomes higher at the consumption point in order to deliver maximum value to the customer in profitable manners. Aworh (2015) defined agriculture VA as “any act to enhance the shelf life of perishable products, lessen the rate of postharvest losses, and boost its demand in the market, which improve Agribusiness
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the firm’s profitability”. Although Paraman, Sharif, Supriyadi, and Rizvi (2015) refers it as “a process of transforming raw farm products into more variety and value-oriented processed products”. Commonly, added value guides the customers to make purchasing decisions rationally. But in this context, two questions arise, “what is the customer’s value and what makes firm’s products and services valuable” (Anderson & Hanselka, 2013). Thus, taking VA into account in practice, companies need broadening value-added strategies to deliver unprecedented values to their customers. Today, consumers are behaving rationally and compare the product price with its worth and have an individualistic perception (Manning, 2015), which communicates their expectations related to product and service level (Anderson & Narus, 1998). However, many consumers have shown willingness to pay more for higher value-added products (Servaes & Tamayo, 2013). A wider range of issues such as organic production and processing, quality assurance, hazard control, food security (Kuzelov, Vailev, Naseva, Taskov, & Dusica, 2015), food availability, packaging, labeling, marketing, and sale, etc. should be incorporated in value-added process (Singh & Chaurasiya, 2014). According to Murthy and Naidu (2012), VA can be achieved through valorization strategy, technology integration, food processing, and proper waste management. This means agriculture value-added SC calls for sustainable performance. Meanwhile, infrastructure, technology, and expertise are basic but important inputs to upgrade VA. Nevertheless, many developing countries are lacking these inputs (Nicita, Ognivtsev, & Shirotori, 2013). The subject of farm VA is considered as quite new, interesting, and has been growing rapidly over the past few years (Alonso & Northcote, 2013). Indeed, VA has significant implication for those farmers, suppliers, processors, distributors, and retailers who incorporate high growth rate, high market share, higher customer satisfaction, and sustainability issues in their business plans. The review of different VA aspects of farm supply highlighted that VA can be interwoven between different stages of food SC (Trienekens, 2011). At present, the business organizations are aware about the importance of value-added practices of their SC partners in their own profitability and survival seems difficult in today’s highly global competitive market environment without delivering unique value experiences to the customers (Trienekens, 2011). This is inferring that the organizations need to incorporate those VA traits that are beyond their own frontiers with the help of SC partners. This could be a win-win object for the whole farm SC. Chen, Zhang, and Delaurentis (2014) quoted the case of the adulterated milk incident of Sanlu Group, in 2008. In this case, the firm’s dairy products were detected with high level of Melamine and Fonterra. For this, the Sanlu Group blamed the farmers and their suppliers to supply them contaminated raw milk. This means that the trustworthiness and transparency among partners are two main pillars of values-based SC. Likewise, performances of firm’s partners affect the entire organizational competence, customer satisfaction, and overall SC performance (Choi et al., 2001). Therefore, the companies need to fix some standards for partners’ selection and should regularly evaluate their partners’ performance meticulously. From the above discussion, it is clear that the SC management has the dual responsibility of improving their partners’ valueadded performance and own value-added performance. Thus, helping the farm SC partners in identifying the importance of adding value, guiding them on what is the exact definition of VA practices, and stimulating them to make superior improvement in their own VA levels are key areas that companies now need to report. Lea and Worsley (2006) addressed the role of farmer’s and processor’s VA and demonstrated that the greater the customer’s value, the higher the customer’s satisfaction. Stevenson and Pirog (2006) revealed that only the processing of by-product is not a VA. Although processing helps in improving shelf life and producing product variety, factors such as cattle’s medical care (Tanchev, 2015), food safety, product sorting, cooling, price, product differentiation, lead time, location, and marketing are integral part of value-added food SC (Wang & Li, 2012). Similarly, the value of unprocessed and processed farm products and the value of their distribution cannot be left on the chance. Local, national, regional, and international values-based strategic Agribusiness
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alliances among farmers, suppliers, processors, distributors, and retailers can help in delivering novel value to the consumers (Stevenson & Pirog, 2013). Matthews (2013) categorized the VA into two phases: buyer’s value phase and sustainability value phase. Anderson and Hanselka (2013) contended with five plans of VA; operational, manpower, sales, managerial, and financial plans. Deloitte (2013) highlighted the role of packaging in VA and stressed that quality packaging supports to various SC levels likewise material handling, production, storage, distribution, and retail. Aung and Chang (2014) pointed out that traceability should be applied as a method to ensure food quality and safety to take consumers’ in confidence. Nevertheless, reducing variability, lead time, improving delivery reliability, repeatability, and reducing cycle time are complex challenges in front of corporate houses (Gunasekaran, Lai, & Cheng, 2008). Gruber and Panasiak (2011) revealed that many national and international bodies have formulated food SC standards to protect the customers. This regulatory pressure is very crucial as it coerces firms to adhere specific standards such as quantity, purity, packaging, and labeling (Tibola, Cunha, Fernandes, & Guarienti, 2016). Hence, the regular quality improvement will be a tool for securing organizational competence. Besides, inefficiency of firms to maintain the standard can lead toward lowering down the product price that may not be sufficient to cover the production cost Zhu et al. (2008). Here, the sustainable VA can minimize the risk of product rejection either by the customers or by the regulatory bodies. In present market scenario, competition among the food retailers is not only about lowering the cost, but also to provide retail standard services (Fearne, Hornibrookm, & Dedman, 2001). Customers disclaim the unhygienic retail products involving intolerable bacteria levels, excessive levels of pesticides, and mislabeled. Therefore, these products should be promptly and completely removed from store shelves (Brocklebank, Hobbs, & Kerr, 2008). Not only does the customer, who has paid a premium for a low quality, displeases the received product/service, but also discolors the image of the entire industry as customers lose faith in suppliers. Brandenburg (2014) revealed that many firms failed in taking cost advantage and managing their working performance, which, in turn, gave rise to considerable value losses. Besides, the customer philosophy of consuming high value-added farm products can encourage firms to focus on regular improvement in their performance as well as their partners’ performance. Interorganizational information exchange system needs best strategies, collaborative forecasting, understanding of own role, and control over information sharing cost. Advanced planning and exact scheduling can build the value-added competitive advantage (Ivert & Jonsson, 2010). According to Bharti, Agrawal, and Sharma (2014), the VA plays an important role to cope with problem of cost and to enhance the customers’ satisfaction. Based on what received above, this paper is an attempt to realize the importance of VA at different stages of farm food SC, and also to see what the VA practices means across the food chain. To do so, this paper tests the food chain VA relationships of different SC players from farms to retailers. 3. CONCEPTUAL MODEL
At this junction, on the basis of reviewed literature, an attempt has been made to develop a framework for this study. 3.1 Farmer’s VA
In the chain-spread activities, performance of downstream partners highly relies on operations of upstream partner. The farmer is an important link of food SC. The profit rate of farm producers is generally less as compared to others SC partners. Hence, adding value in byproducts can assist in increasing income, diversifying production, and in entering new markets (Rosairo, Lyne, Martin, & Moore, 2012). Deloitte (2013) highlighted that the protein contents can improve the value of foodstuffs. Similarly, the cleaning, washing, and sorting activities Agribusiness
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enhance shelf life and mitigate farm products wastage (Tibola et al., 2016). However, the less awareness of farmers is one major hurdle that is confining the firms to make their product more valuable. The reduction in cost at farmer stage without making any reduction in product quality could be profitable for entire SC. Moreover, the proper medical treatment and high cattle’s breed selection may significantly assist the farmers to take the suppliers, processors, and distributors in confidence (Deloitte, 2013). However, the research on farmer VA does not present clearly its role in today’s farm business (Elizabeth, Farina, & Reardon, 2000; Kumar, Singh, Kumar, & Mittal, 2011). Herein, we expect the farmer’s VA practices to be positively related to VA practices of supplier, processor, and distributor. Thus, we have hypothesized: H1: Farmers’ VA is positively related to suppliers’ VA. H2: Farmers’ VA is positively related to processor’s VA. H3: Farmer’s VA is positively related to distributor’s VA. 3.2 Supplier’s VA
In business, suppliers play major roles in value-adding activities and business growth. If the suppliers add values through delivery of raw products, regularly, promptly, accurately, and at low cost, it could reduce the processors’ cost and wastage (Choi, Wu, Ellram, & Koka, 2002; Porter & Kramer, 2011). The suppliers’ value-based integration could provide long-term benefits (Gunasekaran et al., 2008), which may lead toward competitive advantage (Porter, 1985). Moreover, processors look forward for those suppliers who have capability to enhance their overall SC value. Therefore, we have proposed the following hypotheses: H4: Supplies’ VA is positively related to processors’ VA. 3.3 Processor’s VA
The processor stage contributes in enhancing the shelf life of perishable commodities, variety production, and fulfills the different food requirements. At the present time, cost, quality, and service VA should be taken as strategic goal and business objectives of processing organizations. According to Chen et al. (2014), variability in processing technology and poor input may result in the low standard product, which does not meet the quality standards and retail expectations. This may, in turn, increase the rate of order return, return handling cost, and customer dissatisfaction. Du, Baneraurjee, and Kim (2013) emphasized that the price discount and credit option can improve the coordination between the processor and distributors. Veena, Nagendra, and Venkatesha (2011) inferred that farm product differentiation affects purchasing confidence of downstream partners. Du et al. (2013) addressed that offering something special to the customer is an important strategy to develop long-term SC relationships. Bitner, Faranda, Hubbert, and Zeitham (1997) contended on service VA and quoted that higher value-added service has positive correlation with consumption satisfaction. It is clear that processing plays an important role in food industry; however, there is no available study that highlights the relationship between processor and distributor VA practices. Thus, we expect processor’s VA practices to be positively related to distributor VA practices. We have therefore predicted the following: H5: Processor’s VA is positively related to distributor’s VA. 3.4. Distributor’s VA
The effective product distribution works like a pillar in success of any business entity. The delivery of high-level VA by the distributor to retailers means that the distributor is able to satisfy retailers’ expectations in the term of quality, quantity, time, price, and delivery. If the distributor supplies a lot to the retailer in which some items may not meet the ordered specification, then Agribusiness
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VALUE-ADDING PRACTICES IN FOOD SUPPLY CHAIN
H1
Supplier’s value addition
Farmer’s value addition
H3
121
H2
H4
Processor’s value addition
H5
Distributor’s value addition
H6 Retailer’s value addition
Figure 1
Conceptual Framework.
it can affect the rate of profitability (Chen et al., 2014). Besides, the distributors need to deliver their order promptly to satisfy the requirement of downstream parties. The high distributor lead time can create anxiety in the retailer due to the probability of retail sale losses (Martinez, Poole, & Skinner, 2006). Although distributors bundled the products of multiple companies to maximize their brand value, high efforts should be made to boost or at least maintain the quality, quantity, and shelf life of temperature-sensitive products (Teseng, Taylor, & Yue, 2005). Otherwise, it can terminate SC relation. Herein, we expect the distributor’s value-added practices to be positively related with retailer’s value-added practices. Hence, we have put forward the last hypothesis as below: H6: Distributor’s VA is positively related to retailer’s VA. The above six discussed hypotheses, taken together, support the food chain VA framework as depicted in Figure 1, where an overview of the conceptual model including different food SC partners is considered to consolidate the VA practices. 4. RESEARCH METHODOLOGY
The objective of this study is to understand interwoven of value-added practices of food SC partners. It tends to develop novel insights about the notion of a food VA as a weapon to survive and thrive in a competitive market through a conceptual identification of most suitable VA practices. The study applies potential tools to come up with generalizable answers about how food VA should be managed in different SC stages. The items of instrument are quoted in the online Appendix. The structural equation modeling (SEM) tool was employed to test all six proposed research hypotheses. The VA practices for each stage were explored through an extensive literature survey and brief discussions with food SC practitioners. This helped us to categorize the VA practices and to list them in the survey instrument. The instrument was taken on 7-point Likert Scale (1 = Not at all important to 7 = Extremely important) and translated into three languages; Hindi, English, and Punjabi in order to eliminate the language bias. In a prepilot study, seven academicians evaluated the instrument’s items. On the basis of their suggestions, we improved the study instrument and Agribusiness
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later on, sent it for the pilot study. After the satisfactory pilot study results, a survey was done on the large scale within Northern India. Based on the experts’ recommendations, it was decided to include all food SC stages practitioners, namely, farmers, suppliers, processors, distributors, and retailers, as survey respondents. These practitioners were engaged in fruits and vegetables, poultry, and dairy food SC. The random and snowball sampling technique were taken into consideration to contact sample units. The total 735 questionnaires were distributed, but the only 463 questionnaires returned. This shows 62.99% of the survey response rate. Here, only 452 responses were digitized in statistical package of social science (SPSS) as 11 responses were incomplete. The findings show that total 86 (19.02%) of respondents belong to farmers, 89 (19.69%) belong to suppliers, 64 (14.15%) belong to processing practitioners, 111 (24.55%) belong to distributors, and 102 (22.56%) lie under retail managers category. Further, the descriptive stats, factor analysis and SEM techniques were employed to analyze the valid survey responses. 5. EMPIRICAL ANALYSIS
This section analyzes the valid survey responses with appropriate statistical tool and techniques. 5.1 Goodness of Measure
Here, factor analysis was applied to extract the features of each food SC stage. Moreover, Croanbach alpha, Eigen Commonality and Alpha if the item deleted values were used to measure the internal consistency of homogeneity among the items. 5.1.1 Factor Analysis for Farmer’s Stage VA. Factor analysis with varimax rotation was done to
validate the dimensionality and the appropriateness of the measurement scale. This stage had eigenvalues greater than 1.00 and it explained 73.25% of cumulative variance. The KMO (Kaiser–Meyer–Olkin of measuring sampling adequacy) measure indicated sampling adequacy of 0.930 meaning sufficient intercorrelation. In addition, Barlett’s test of sphericity was significant (Chi square = 3,513.75, p < 0.001). There were 11 items in farmers’ VA and two factors were extracted with significant loading range, namely, fruits and vegetables and milk (Table 1). 5.1.2 Factor Analysis for Supplier’s Stage VA. In this part, the factor analysis was used to
identify the behavior of supplier’s value-added practices. This stage consists seven items. The analysis clubbed all these items into a single factor. As a result, the process was unable to produce rotated matrix; hence, the component matrix was taken into consideration for the factor loading. The factor obtained the eigenvalue of 3.82 and explained 63.72% of total variance. The Barlett’s test of sphericity was significant (Chi square = 1326.46, p < 0.001). Here, obtained supplier’s stage VA results are depicted in Table 2. 5.1.3 Factor Analysis for Processor’s Stage VA. When processor’s VA practices were subject to
the factor analysis, two factors were extracted with significant loadings. The results showed that both factors had eigenvalues greater than 1.0. Here, the total explained variance was 72.76%. The KMO measure of sampling adequacy was 0.952 indicating sufficient intercorrelation. Besides, Barlett’s test of sphericity was significant (Chi square = 5,003.63, p < 0.001). After the consideration of the statements, the two factors were named as service addition and product addition as highlighted in Table 3. 5.1.4 Factor Analysis for Distributor’s Stage VA. The distributor stage was initially consisted of
seven items. Here, in reliability analysis, it was found that bundling and unbundling (D1) had low individual commonality and alpha values below 0.5. Thus, this item was not considered for analysis. After elimination of this item, the total six items were factor analyzed. The analysis Agribusiness
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VALUE-ADDING PRACTICES IN FOOD SUPPLY CHAIN TABLE 1.
Factor Analysis for Farmer’s Stage VA Practices
Item No.
Items
F3 F5 F4 F6 F1 F2 F8 F10 F9 F11 F7
TABLE 2.
123
Fruits and Vegetables
Sorting 0.884 Grading 0.864 Quality 0.855 Packaging 0.846 Organic farming 0.832 Cleaning and washing 0.811 Good living environment for cattle Storage Purity Free delivery Proper feeding and medical care Eigenvalue 5.870 Cronbach’s Alpha 0.936 Percentage variance (73.25%) 53.367 KMO = 0.930, Barlett’s test of sphericity = 3,513.75, DF = 55, Sig. 0.000.
Milk
0.840 0.827 0.819 0.796 0.784 2.187 0.831 19.883
Factor Analysis for Supplier’s VA Practices
Item No. S7 S4 S3 S1 S5 S2 S6
Supplier VA
Items Use of cold chain Location Lead time Supplier’s product quality Supplier cost Regular supplies Quantity discounts Eigenvalue Cronbach’s Alpha Percentage variance (63.72%) KMO = 0.894, Barlett’s test of sphericity = 1,326.46, DF = 15, Sig. 0.000.
0.848 0.827 0.789 0.778 0.774 0.769 0.614 3.82 0.885 63.72
extracted one single factor for this stage as highlighted in Table 4. Here, due to extraction of one single factor, the component matrix was used. All retained six items had significant loadings. This construct explained the total 78.17% of cumulative variance (Table 4). 5.1.5 Factor Analysis for Retailer’s Stage VA. In this last phase of the factor analysis process,
the retailer’s VA practices were taken under consideration. The initial reliability evaluation did not allow retaining two items named as membership (R7) and electronic payments (R9). These both items had low individual commonality and alpha values below 0.5. Therefore, both were eliminated and the remaining seven items retained to be subject of factor analysis. Furthermore, analysis extracted again on single factor with seven items, so that component matrix was used for items loadings. All the items were significantly loaded above 0.5. Here, the Chi square value was 1,667.23 that is significant p < 0.001. The construct explained the total 62.78% of cumulative variance (Table 5). 5.2 Reliability
The interitem consistency measure of Croanbach’s alpha was taken into consideration to access the reliability of all study items. All the reliability alpha values are above 0.80, meaning that all items are reliable (Hair, Black, Babin, Anderson, & Tatham, 2009). The uppermost Croabach’s Agribusiness
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Factor Analysis for Processor’s VA Practices
Item no.
Items
P4 P11 P12 P13 P14 P7 P3 P6 P2 P8 P10 P9 P1 P5
TABLE 4.
Service Addition
Processing cost 0.881 Processor’s lead time 0.877 Credit sale option 0.870 Electronic data interchange 0.847 Advance shipment notifications 0.841 Marketing 0.840 Brand 0.715 Product variety Quantity Packaging Processed product quality Product differentiations Innovation Labeling Eigenvalue 7.361 Cronbach’s Alpha 0.943 Percentage variance (72.76%) 52.575 KMO = 0.952, Barlett’s test of sphericity = 5,003.75, DF = 91, Sig. 0.000.
Product Addition
0.866 0.847 0.826 0.810 0.802 0.793 0.770 2.826 0.929 20.186
Factor Analysis for Distributor’s VA Practices
Item No. D4 D6 D3 D5 D2 D7
Items Quantity Flexibility Shipping notification Cooling Shipping accuracy Sale credits Eigenvalue Cronbach’s Alpha Percentage variance (78.17%) KMO = 0.934, Barlett’s test of sphericity = 2,391.44, DF = 15, Sig. 0.000.
Distributor VA 0.916 0.908 0.894 0.881 0.874 0.829 4.69 0.939 78.17
alpha value was obtained for service addition of the processor’s stage (0.943, see Table 3) and lowest Croabach’s alpha value was obtained for the milk factor of the farmer’s stage (0.831, see Table 1). The high score of Croanbach’s alpha for all study items under the survey specifies that the statements are consistent and reliable. 5.3 Results of the Structural Model and Discussion
The conceptual framework of this study is illustrated in Figure 1. The framework consists of six hypothesized relationships between farmer, supplier, processor, distributor, and retailer’s VA practices. Figure 2 represents the SEM analysis. The quantity discount offer (S6) was eliminated to due to its low loading value and thus to get better model fit. In the estimation, all the study measurements have statistically significant loading values to their second-order corresponding construct. Generally, the model is a good fit as it has CMIN/DF (Chi-square/degree of freedom) = 1.846, p = 0.000, GFI (goodness-of-fit statistic) = 0.864, AGFI (adjusted goodnessof-fit statistic) = 0.846, and CFI (comparative fit index) = 0.951. The root mean square error of approximation (RMSEA) value is only 0.044. This is very good as RMSEA should be below Agribusiness
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VALUE-ADDING PRACTICES IN FOOD SUPPLY CHAIN TABLE 5.
Factor Analysis for Retailer’s VA Practices
Item No. R2 R4 R3 R1 R6 R5 R8
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Retailer VA
Items Retail displays Product cooling Product variety Strategic retail locations Regular promotions Retail price Product availability Eigenvalue Cronbach’s Alpha Percentage variance (62.78%) KMO = 0.921, Barlett’s test of sphericity = 1677.23.46, DF = 21, Sig. 0.000.
Figure 2
0.844 0.843 0.824 0.786 0.775 0.745 0.721 4.39 0.900 62.78
Structural Equation Model.
0.050. All these values are acceptable according to model fit recommendations of Hair et al. (2009). The results of hypotheses testing through SEM are highlighted in Table 6 that affirms all six hypotheses. The Hypothesis 1 infers that farmer VA is positively related to supplier VA. The standardized coefficient is 0.50 and C.R. is 6.51 that is statistically significant at p 1.96. From the Hypothesis 1, it can be interpreted that the organic farming by farmers may help in fulfilling the current market demand of organic foodstuff. Moreover, the cleaning, sorting, and grading lead toward better product presentation. The higher level of VA at farmer stage may lead to higher levels of VA in supplier stage. Agribusiness
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Results for Proposed Structural Equation Model
No. H1 H2 H3 H4 H5 H6
Hypotheses Farmer VA → supplier VA Farmer VA → processor VA Farmer VA → distributor VA Supplier VA → processor VA Processor VA → distributor VA Distributor VA → retailer VA
Effect
C.R.
Sig.
Remark
0.50 0.37 0.22 0.51 0.63 0.58
6.51∗
0.000 0.000 0.004 0.000 0.000 0.000
H1 is supported H2 is supported H3 is supported H4 is supported H5 is supported H6 is supported
4.51∗ 2.85∗ 7.37∗ 7.82∗ 11.49∗
The Hypothesis 2 is also affirmed by the analysis, which infers that farmer’s VA is positively related to processor’s VA. The standardized coefficient is 0.37 and C.R. is 4.51 that is statistically significant at p
