Customer Satisfaction Parameters for Fruits and Vegetables Retail- An AHP Approach

Transcription

1 Customer Satisfaction Parameters for Fruits and Vegetables Retail- An AHP Approach Dr. Lamay Bin Sabir Assistant Professor, Al-Barkaat Institute of Management Studies, Aligarh Abstract In today s competitive and challenging market, customers have the flexibility to choose when and where to buy. On the other hand, sellers are feeling the pressure day by day to increase customer satisfaction and deliver the services accurately and efficiently. When the product is perishable, having short shelf life, sellers need to device dynamic strategies in order to deal with the competition and increase customer satisfaction, resulting in customer retention. Simple customer is now changing towards strategic customer where they have ample amount of sellers selling their product. Therefore the importance of understanding the need of the customer is important for the sellers. This paper identifies the main parameters of customer satisfaction vis-à-vis perishable products, specially fruits and vegetables. Main parameters of customer satisfaction are ranked using Analytical Hierarchy Process to identify the importance of the parameter in the minds of the customer. This paper aims to develop a hierarchy of parameters important for customer satisfaction with respect to two types of retail sectors selling fruits and vegetables (organized and unorganized). Keywords: AHP, Perishable, Fruits and vegetables, Customer satisfaction. 1. Introduction Fornell et al. (1996) said that there are factors on which customer satisfaction is heavily dependent are retailer s performance on helping the customer s perceptions and expectation of the customer towards the perishable product inside a retail store. It is supposed that higher the customer satisfaction levels, higher will be the customer loyalty for the retail stores (Anderson and Fornell, 2000; Juhl et al., 2002). According to Bolton (1998), customer Lamay Bin Sabir Page 6

2 satisfactions as well as customer loyalty are parameters which are critically important in the success of any business to survive. Without them secure future revenues will be at stake. In another study by Sweeney et al. (1999) found that perceptions of apparent risk and product quality have positive and negative impact on perceived value for money and hence influences consumer s motivation to buy. The basic notion is that customer will compare the services of different sellers according to the commodity. Wang D. (2014) argued that customer satisfaction is basically the difference between the expected and observed value of purchased goods and/ or services according to customer perception. Analytical hierarchy process is typically a technique for analyzing and organizing complex problems of psychology and mathematics. In this process, the combinations of individual performance parameter with one other key performance parameter are analyzed. One parameter is preferred and a weight is assigned to each parameter. Therefore the process of AHP is mainly the calculation of weights. Ratios are considered for paired comparison. According to Li et al. (2014) and Kilincci & Onal (2011) the inputs for AHP are criterions and alternatives. The pair wise comparisons are done at each level of a hierarchy with respect to the ultimate goal. This comparison is conducted using 9 point scale developed by Saaty (1980). The scale expresses preference between options as equally, moderately, strongly, very strongly or extremely preferred. These preferences of the respective comparisons are translated into pair wise weights of 1, 3, 5, 7 and 9 respectively, with 2, 4, 6, and 8 as intermediate values which can be used also. In this paper, two different types of retail stores viz., Organized and Unorganized are considered which are selling fruits and vegetables. Customers who are buying from these two retail sectors consider different factors regarding their purchase. Customer satisfaction is an important criterion for success for both sectors. The criterions considered for customer satisfaction are Pricing, Hygiene, Availability, Shelf life and Information on packaging. This paper proposes a method for recommending in which retail store type customers, what are their priorities regarding the parameters that are discussed above. The organization of this paper is viz., section 1 gives introductory details, section 2 gives brief literature review, section 3 give details about the used methodology for analyzing problem under consideration, section 4 shows results and analysis, section 5 contains conclusions and future research directions. Lamay Bin Sabir Page 7

3 2. Literature Review Bolten (1998) said that customers repeat purchase product or service only when they are satisfied, which may result in customer loyalty towards the business (Juhl et al., 2002). For improving customer retention, the positive appraisal resulting from customer satisfaction is the key which will further strengthens the relationship between customer and retailer (Fornell, 1992; Bolton, 1998). All these factors significantly affection the performance of business. Huddleston et al. (2008) said that customer satisfaction impact due to price differs among store formats. Customers who are purchasing from speciality stores are much more satisfied with the prices than conventional grocery stores. This can be the case since customers may be ready to pay more for better quality options and the perception of customers towards speciality stores that they have higher quality products. A study done by Salvador et al. (2007) argued that customers satisfaction can be achieved when the prices they are paying for the product is sufficient as per their perception. As per Hermann et al. (2007), customer satisfaction is directly proportional to perceptions of prices vis-à-vis quality. Harmon and Foote (2004) said that satisfaction levels decrease when customers have to pay more for an item that they perceive to having the same quality with the one selling at lower prices. Other factors like hygiene, information on packaging were discussed with the field experts. They also proposed shelf availability as one of the major factor of customer satisfaction inside a retail store. Chung and Li (2013) in their paper analyzed the impact of dynamic pricing strategies on consumer perceptions for perishable foods. Interviews were conducted by them from three food retail managers in South Korea so that practical aspect of the perishable food inventory management can be collected. Retail manager s inputs were used to design the survey for the consumers in South Korea so that their perceptions towards two different strategies can be collected. Prioritizing customer requirements could be viewed as a complex multi criteria decision making problem. Lu et al. (1994) used AHP as a multi criteria decision making technique to weigh customer requirements. Aswad (1989) proposed an integration of AHP with the determination of customer requirements. According to Saaty and Kriti (2008) AHP is a widely used technique for multi-criteria analyses of alternatives that can support decision makers examine the interaction of multiple factors in complex Lamay Bin Sabir Page 8

4 situations. AHP has been applied to numerous decision making problems in the previous studies which involve ranking, resource allocation, prioritization, quality management and benchmarking (Bhushan and Rai, 2004; Forman and Gass, 2001; Wei et al., 2005). Figure 1 shows factors that impact customer satisfaction for purchasing fruits and vegetables in a retail store. Customer satisfaction has been studied by many authors but there seems to be a scarcity of literature regarding the priority of customer satisfaction parameters for fresh fruits and vegetable purchase. This paper tries to deal with these issues to help the retailers to understand the customer satisfaction in a better way. Figure 1: Factors impacting customer satisfaction in case of F&V retail Hygiene Availability Information Percieved Quality (Shelf Life) Customer Satisfaction in a Retail Store Dynamic Pricing 3. Research Methodology The basic objective of this study is to understand the priorities of customers when they buy fruits and vegetables. What parameters they prefer more over other parameters. This would help the retailer to better understand the need of customers so that they can devise strategies keeping in mind the current priorities of the customer. Data is collected from customers purchasing fruits and vegetables from both organized retail store and unorganized retailer in New Delhi. Since subject expert is required in a technique like AHP, here to understand the customer perception, customers are contacted inside retail store that are actually doing the purchase of fruits and vegetables. Satty and Kriti (2008) proposed six steps in implementing AHP which are: 1. Choose the requirements to be prioritized. 2. Set the requirements into the rows and columns of the n x n AHP matrix. Lamay Bin Sabir Page 9

5 3. Perform a pair-wise comparison of the requirements in the matrix according to a set of criteria. 4. Sum the columns. 5. Normalize the sum of rows. 6. Calculate the row averages. After following the above mentioned steps, other parameters of AHP like Consistency Index (CI), Consistency Ratio (CR) etc are calculated and the values are verified, which are explained, in the subsequent sections of this paper. After the development of all the matrices, the maximum eigen value (λmax) for matrix is calculated. The λmax value is an important validating parameter in AHP (Saaty, 2000). It is used for calculating CR of the estimated vector in order to authenticate whether the pair-wise evaluation matrix provides a consistent evaluation or not. To calculate the CR, steps that are followed are listed below: 1. Calculate the eigenvector or the relative weights and λmax for matrix of order n 2. Compute the consistency index for each matrix of order n by the formulae: CI = (λmax -n)/ (n-1) 3. The consistency ratio is then calculated using the formulae: CR = CI/RI where RI is the Random Consistency Index (RI) varies depending upon the order of matrix. The acceptable CR range varies according to the size of matrix i.e for a 3 by 3 matrix, 0.08 for a 4 by 4 matrix and 0.1 for all larger matrices, n> 5 (Saaty, 2000). An acceptable consistency ratio helps to ensure decision-maker reliability in determining the priorities of a set of criteria. Table 1 shows the criteria and the abbreviations used in order to take responses from the customers on the numerical scale provided for the study. To avoid any confusion from the customer s side, the author administered the questionnaire personally in order to make the respondent comfortable. Table 1: Criteria and Abbreviation Used Lamay Bin Sabir Page 10

6 S.No. Criteria Abbreviation Used 1 Price of fruits and vegetables, includes discounts and other promotional activities Price 2 Hygiene of fruits and vegetables Hygiene 3 Availability of fresh fruits and vegetables at shelf Availability 4 Remaining shelf life of fruits and vegetables Shelf Life 5 Information regarding the packaging and shelf life Packaging 4. Results and Analysis AHP is used as a tool for systematically analyzing the opinions of several experts belonging to diverse fields. Consulting more experts will avoid bias that may be present when the judgments are considered from a single expert. Here in our case, customers are contacted in order to get inputs. Hence, customers purchasing from both type of retail stores (organized and unorganized) are selected as the experts for the decision-making. The nominal-ratio scale of 1 to 9 (Saaty, 1994) is adopted for pair wise comparison of the questionnaires. Customers were asked to conduct a pair wise comparison of the five criteria s. All the responses were collected for both type of retail segments and recorded. The results of pair wise comparisons are filled in positive reciprocal matrices to calculate the eigenvector and eigen value (Table 2 to 6). The consistency of the judgments is determined by a measure called consistency ratio (C.R.). First responses from the organized sector are analyzed. Table 2: Opinion of Participant 1 Price Availability Hygiene Shelf Life Packaging Price 1 1/ Availability /2 6 Hygiene 1/3 1/6 1 1/4 5 Shelf Life ½ Packaging 1/5 1/6 1/5 1/7 1 Table 3: Opinion of Participant 2 Price Availability Hygiene Shelf Life Packaging Price 1 1/8 1/7 1/3 3 Availability Lamay Bin Sabir Page 11

7 Hygiene 7 1/ Shelf Life 3 1/8 1/7 1 7 Packaging 1/3 1/7 1/8 1/7 1 Table 4: Opinion of Participant 3 Price Availability Hygiene Shelf Life Packaging Price 1 1/ Availability Hygiene /2 Shelf Life 1/5 1/8 1/6 1 1/2 Packaging Table 5: Opinion of Participant 4 Price Availability Hygiene Shelf Life Packaging Price 1 5 1/3 1/6 6 Availability 1/5 1 1/5 1/7 3 Hygiene /2 5 Shelf Life Packaging 1/6 1/3 1/5 1/6 1 Table 6: Opinion of Participant 5 Price Availability Hygiene Shelf Life Packaging Price 1 1 1/2 1 1 Availability Hygiene 2 1/3 1 1/2 3 Shelf Life 1 1/ Packaging 1 1/4 1/3 1/3 1 For aggregating the individual preferences when more than one expert is asked for the decision making, weighted geometric mean method has been the mostly applied in AHP. To find out the ranks of the alternatives, weighted geometric means of each individual opinion is calculated and final consolidated matrix is developed. Table 7 shows the consolidated matrix for the responses of customer purchasing from organized sector. Table 7: Consolidated Response-Organized Retail Lamay Bin Sabir Page 12

8 Price Availability Hygiene Shelf Life Packaging Price Availability Hygiene Shelf Life Packaging Sum (col) Dividing each row with the respective sum of the column, and then adding the total of each row, we get the priority and the respective rank of the individual dimension of customer satisfaction for organized retail (Table 8). Table 8: Priority and Respective rank of Individual Dimension-Organized Retail Price Availability Hygiene Shelf Life Packaging Priority Rank % Share Price Availability Hygiene Shelf Life Packaging To identify the consistency ratio, we need to calculate the eigen value for the matrix. For this purpose, we need to multiply each value in the first column of the pair wise comparison matrix by the priority of the first item; multiply each value in the second column of the pair wise comparison matrix by the priority of the second item; continue this process for all columns of the pair wise comparison matrix. Sum the values across the rows to obtain a vector of values labeled weighted sum. This computation for the customer satisfaction problem is as follows: = { 0.53} { 0.41} { 0.44} { 0.42} { 1 } Lamay Bin Sabir Page 13

9 = { 0.48} { 0.56} { 0.52} { 0.44} { 0.50} = { 2.50} Divide the elements of the weighted sum vector obtained above by the corresponding priority for each criterion. Price = = Availability = Hygiene = Shelf Life = Packaging = = = = = The maximum value of eigen vector for the above matrix, λ max = ( ) / 5 = Consistency index, C.I. = (λmax -n)/ (n-1) = ( ) / (5-1) = Random Index for the matrix of order 5, R.I. = 1.12 Consistency Ratio, C.R. = C.I./ R.I. = / 1.12 = A consistency ratio of 0.10 or less is considered acceptable. Because the pair wise comparisons for the customer satisfaction criteria show CR as , we can conclude that the degree of consistency in the pair wise comparisons is acceptable. Same process was carried out for inputs for unorganized retail store customers and the consolidated matrix for the same is shown in Table 9. Table 9: Consolidated Response- Unorganized Retail Price Availability Hygiene Shelf Life Packaging Price Availability Lamay Bin Sabir Page 14

10 Hygiene Shelf Life Packaging Sum (col) Table 10 shows the priority and the respective rank of the individual dimension of customer satisfaction for unorganized retail. Table 10: Priority and Respective rank of Individual Dimension-Unorganized Retail Price Availability Hygiene Shelf Life Packaging Priority Rank % Share Price Availability Hygiene Shelf Life Packaging The maximum value of eigen vector for unorganized sector, λ max = Consistency index, C.I. = (λmax -n)/ (n-1) = ( ) / (5-1) = Random Index for the matrix of order 5, R.I. = 1.12 Consistency Ratio, C.R. = C.I./ R.I. = / 1.12 = A consistency ratio of 0.10 or less is considered acceptable. Because the pair wise comparisons for the customer satisfaction criteria show CR as 0.037, we can conclude that the degree of consistency in the pair wise comparisons is acceptable. Figure 2: Percent Share for Organized Retail Retail Figure 3: Percent Share for Unorganized Lamay Bin Sabir Page 15

11 Availability 27% Hygiene 23% Information 10% Availability 38% Hygiene 15% Information 5% Shelf Life 21% Customer Satisfaction in a Retail Store Dynamic Pricing 18% Shelf Life 24% Customer Satisfaction in a Retail Store Dynamic Pricing 18% 5. Conclusion and Future Research Directions Today most decisions are taken in a dynamic and complex environment. In case of short shelf life products, the decision makers mostly make decisions based on previous experience as another complexity in terms of shelf life is added in the system. From Table 8 and 10, both types of retail store customers were asked about their priority of parameters depending upon the satisfaction level. For unorganized as well as organized sector, availability holds the key. Customers prefer availability of the fruits and vegetables they want to buy. This can be understood as the basic purpose of running into a retail store is the need of that particular product, without which other parameters won t have any usefulness for the customer. In case of organized retail, second priority is given to hygiene. This means that customers basically visit retail stores so that they can purchase in hygienic conditions whereas hygiene is ranked 4 (Table 10) in unorganized sector. Second priority is given to shelf life in case of unorganized retail implying customers are more concern about shelf life of the product they are purchasing. Shelf life is third priority (Table 8) for customers purchasing from organized retail as they may understand that organized retail stores are more concerned about the freshness of the product. Price is ranked fourth in priority for organized retail stores. This could have been the case because customers purchasing in organized retail segments must be ready to pay premium price in order to get extra benefits like freshness, hygiene etc. In case of unorganized sector, pricing is ranked third in priority implying that customer purchasing from unorganized sector are much more sensitive towards price of fruits and vegetables. Lamay Bin Sabir Page 16

12 Future researchers can add more parameters of customer satisfaction with different levels of hierarchy. A comparative study for two organized retail stores can also be carried out in future in order to better understand the perceptions of the customer. References Anderson, E. W., & Fornell, C. (2000). The customer satisfaction index as a leading indicator. Handbook of service marketing and management. London: Sage Publications, Aswad, A. (1989). Quality function deployment: A systems approach. In Proceedings of the 1989 IIE Integrated Systems Conference, Institute of Industrial Engineers, Atlanta, GA, pp Bhushan, N. & Rai, K. (2004). Strategic Decision Making: Applying the Analytic Hierarchy Process, London, Springer-Verlag, Bolton, R. N. (1998). A dynamic model of the duration of the customer's relationship with a continuous service provider: the role of satisfaction.marketing science, 17(1), Chung, J., & Li, D. (2013). The prospective impact of a multi-period pricing strategy on consumer perceptions for perishable foods. British Food Journal,115(3), Forman, E.H. and Gass, S.I. (2001). The analytical hierarchy process an exposition, Operations Research 49 (4), Fornell, C., Johnson, M. D., Anderson, E. W., Cha, J., & Bryant, B. E. (1996). The American customer satisfaction index: nature, purpose, and findings. The Journal of Marketing, Harmon, S. K., & Foote, D. A. (2004). The influence of price difference and equity sensitivity on customer satisfaction in a dynamic pricing environment.advances in Consumer Research, 31(1), Hermann, A., Xia, L., Monroe, K. B., & Huber, F. (2007). The influence of price fairness on customer satisfaction: an empirical test in the context of automobile purchases. Journal of Product & Brand Management, 16(1), Hua Lu, M., Madu, C. N., Kuei, C. H., & Winokur, D. (1994). Integrating QFD, AHP and benchmarking in strategic marketing. Journal of Business & Industrial Marketing, 9(1), Lamay Bin Sabir Page 17

13 Huddleston, P., Whipple, J., Nye Mattick, R., & Jung Lee, S. (2009). Customer satisfaction in food retailing: comparing specialty and conventional grocery stores. International Journal of Retail & Distribution Management, 37(1), Juhl, H. J., Kristensen, K., & Østergaard, P. (2002). Customer satisfaction in European food retailing. Journal of Retailing and Consumer Services, 9(6), Kilincci, O., & Onal, S. A. (2011). Fuzzy AHP approach for supplier selection in a washing machine company. Expert systems with Applications, 38(8), Li, C., Anavatti, S. G., & Ray, T. (2014). Analytical hierarchy process using fuzzy inference technique for real-time route guidance system. Intelligent Transportation Systems, IEEE Transactions on, 15(1), Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill. Saaty, T.L. and Kirti, P. (2008). Group Decision Making: Drawing out and Reconciling Differences, RWS Publications, Pittsburgh, PA Salvador, C., Rebolloso, E., Fernández-Ramírez, B., & del Pilar Cantón, M. (2007). Service price components and their relationship with customer satisfaction. Journal of Revenue & Pricing Management, 6(1), Sweeney, J. C., Soutar, G. N., & Johnson, L. W. (1999). The role of perceived risk in the qualityvalue relationship: a study in a retail environment. Journal of retailing, 75(1), Wang, D. (2014). The study and use of AHP in logistics and customer satisfaction. Journal of Chemical & Pharmaceutical Research, 6(7). Wei, C. C., Chien, C. F., & Wang, M. J. J. (2005). An AHP-based approach to ERP system selection. International journal of production economics, 96(1), Lamay Bin Sabir Page 18