Strategies to assess generic skills for different types of students

[EN] The Universitat Politècnica de València (UPV) has synthesized a profile to be acquired by all the students based on 13 generic skills. For its assessment, the UPV has also developed a rubric for every skill depending on the level of the course. In this research, we develop an educational innovation for validating the rubrics for 3 of the 13 generic skills specified by the UPV. The chosen skills are: “Ability to think practically and apply knowledge in practical situations”, “Innovation, creativity and entrepreneurship ability” and “Teamwork and leadership ability”. To do this, we develop the same methodology in two groups (Morning/English) of the same course (Marketing Research of the Degree of Business Administration and Management of the Faculty of Business Administration and Management at the UPV) with significantly different student profiles. The assessment results of the skills reveal that there are no significant differences between groups. In conclusion, we could say that the rubrics developed by the UPV are adequate to assess all types of students: Erasmus or non-erasmus, working or having worked in the last 2 years or without work experience, and regardless of their satisfaction with the course.Baviera-Puig, A.; Escribá Pérez, C.; Buitrago Vera, JM. (2017). Strategies to assess generic skills for different types of students. En Proceedings of the 3rd International Conference on Higher Education Advances. Editorial Universitat Politècnica de València. 26-33. https://doi.org/10.4995/HEAD17.2017.4797263

Geomarketing models in supermarket location strategies

[EN] Choosing where to open a new outlet is a critical decision for retail firms. Building on the multiplicative competitive interaction model from retail location theory, this paper develops a geomarketing model that can be used to devise supermarket location strategies. First, attributes that explain a supermarket s pull on consumers were determined. These attributes included objective (taken from databases and empirical observation) and subjective (based on managerial judgements) variables relating to the supermarket and its trade area. Then, geographic information system tools were used to analyse real data at a highly detailed level (road section). From a geomarketing viewpoint, the model shows that sociodemographic characteristics of the supermarket s trade area affect firms location strategies. The paper also discusses improvements for calibrating and validating this model. Adding the spatial organization of supermarkets to the model yields a different consumer behaviour pattern. This geomarketing model can help managers to design supermarket location strategies according to shop features, competitors and environment, whilst estimating supermarket sales.The authors would like to thank the Consum-Universitat Politècnica de València Chair (Cátedra) for collaborating in this study.Baviera Puig, MA.; Buitrago Vera, JM.; Escribá Pérez, C. (2016). Geomarketing models in supermarket location strategies. Journal of Business Economics and Management. 17(6):1205-1221. doi:10.3846/16111699.2015.1113198S12051221176Applebaum, W. (1966). Methods for Determining Store Trade Areas, Market Penetration, and Potential Sales. Journal of Marketing Research, 3(2), 127. doi:10.2307/3150201Baviera-Puig, A., Roig-Tierno, N., Buitrago-Vera, J., & Mas-Verdu, F. (2013). Comparing trade areas of technology centres using ‘Geographical Information Systems’. The Service Industries Journal, 33(7-8), 789-801. doi:10.1080/02642069.2013.740467Blattberg, R. C., & Hoch, S. J. (1990). Database Models and Managerial Intuition: 50% Model + 50% Manager. Management Science, 36(8), 887-899. doi:10.1287/mnsc.36.8.887Campo, K., Gijsbrechts, E., Goossens, T., & Verhetsel, A. (2000). The impact of location factors on the attractiveness and optimal space shares of product categories. International Journal of Research in Marketing, 17(4), 255-279. doi:10.1016/s0167-8116(00)00026-4Cheng, E. W. L., Li, H., & Yu, L. (2007). A GIS approach to shopping mall location selection. Building and Environment, 42(2), 884-892. doi:10.1016/j.buildenv.2005.10.010Clarke, G. (1998). GeoJournal, 45(4), 289-298. doi:10.1023/a:1006995106736Cliquet, G. (1990). La mise en  uvre du modele interactif de concurrence spatiale (MICS) subjectif. Recherche et Applications en Marketing, 5(1), 3-18. doi:10.1177/076737019000500101Cliquet, G. (1995). Implementing a subjective MCI model: An application to the furniture market. European Journal of Operational Research, 84(2), 279-291. doi:10.1016/0377-2217(93)e0313-mColome, R. 2002. Consumer choice in competitive location models: PhD thesis. Universidad Pompeu Fabra, Barcelona.Cooper, L. G., & Nakanishi, M. (1983). Standardizing Variables in Multiplicative Choice Models. Journal of Consumer Research, 10(1), 96. doi:10.1086/208948De Beule, M., Van den Poel, D., & Van de Weghe, N. (2014). An extended Huff-model for robustly benchmarking and predicting retail network performance. Applied Geography, 46, 80-89. doi:10.1016/j.apgeog.2013.09.026Drezner, T., & Drezner, Z. (2002). Annals of Operations Research, 111(1/4), 227-237. doi:10.1023/a:1020910021280Dussart, C. (1998). Category management: European Management Journal, 16(1), 50-62. doi:10.1016/s0263-2373(97)00073-xGautschi, D. A. (1981). Specification of Patronage Models for Retail Center Choice. Journal of Marketing Research, 18(2), 162. doi:10.2307/3150951Ghosh, A. (1984). Parameter nonstationarity in retail choice models. Journal of Business Research, 12(4), 425-436. doi:10.1016/0148-2963(84)90023-7Ghosh, A., Neslin, S., & Shoemaker, R. (1984). A Comparison of Market Share Models and Estimation Procedures. Journal of Marketing Research, 21(2), 202. doi:10.2307/3151702González-Benito, Ó., Greatorex, M., & Muñoz-Gallego, P. A. (2000). Assessment of potential retail segmentation variables An approach based on a subjective MCI resource allocation model. Journal of Retailing and Consumer Services, 7(3), 171-179. doi:10.1016/s0969-6989(99)00026-0Grewal, D., Levy, M., Mehrotra, A., & Sharma, A. (1999). Planning merchandising decisions to account for regional and product assortment differences. Journal of Retailing, 75(3), 405-424. doi:10.1016/s0022-4359(99)00015-9Huff, D. L. (1964). Defining and Estimating a Trading Area. Journal of Marketing, 28(3), 34. doi:10.2307/1249154Kim, H., & Choi, B. (2013). The Influence of Customer Experience Quality on Customers’ Behavioral Intentions. Services Marketing Quarterly, 34(4), 322-338. doi:10.1080/15332969.2013.827068Klapper, D., & Herwartz, H. (2000). Forecasting market share using predicted values of competitive behavior: further empirical results. International Journal of Forecasting, 16(3), 399-421. doi:10.1016/s0169-2070(00)00052-2Kumar, V., & Karande, K. (2000). The Effect of Retail Store Environment on Retailer Performance. Journal of Business Research, 49(2), 167-181. doi:10.1016/s0148-2963(99)00005-3Li, Y., & Liu, L. (2012). Assessing the impact of retail location on store performance: A comparison of Wal-Mart and Kmart stores in Cincinnati. Applied Geography, 32(2), 591-600. doi:10.1016/j.apgeog.2011.07.006Mahajan, V., Jain, A. K., & Ratchford, B. T. (1978). Use of Binary Attributes in the Multiplicative Competitive Interactive Choice Model. Journal of Consumer Research, 5(3), 210. doi:10.1086/208733Mendes, A. B., & Themido, I. H. (2004). Multi-outlet retail site location assessment. International Transactions in Operational Research, 11(1), 1-18. doi:10.1111/j.1475-3995.2004.00436.xMerino, M., & Ramirez-Nafarrate, A. (2016). Estimation of retail sales under competitive location in Mexico. Journal of Business Research, 69(2), 445-451. doi:10.1016/j.jbusres.2015.06.050Murad, A. A. (2003). Creating a GIS application for retail centers in Jeddah city. International Journal of Applied Earth Observation and Geoinformation, 4(4), 329-338. doi:10.1016/s0303-2434(03)00020-5Nakanishi, M., & Cooper, L. G. (1974). Parameter Estimation for a Multiplicative Competitive Interaction Model: Least Squares Approach. Journal of Marketing Research, 11(3), 303. doi:10.2307/3151146Nakanishi, M., & Cooper, L. G. (1982). Technical Note—Simplified Estimation Procedures for MCI Models. Marketing Science, 1(3), 314-322. doi:10.1287/mksc.1.3.314Nakanishi, M., Cooper, L. G., & Kassarjian, H. H. (1974). Voting for a Political Candidate Under Conditions of Minimal Information. Journal of Consumer Research, 1(2), 36. doi:10.1086/208589Roig-Tierno, N., Baviera-Puig, A., Buitrago-Vera, J., & Mas-Verdu, F. (2013). The retail site location decision process using GIS and the analytical hierarchy process. Applied Geography, 40, 191-198. doi:10.1016/j.apgeog.2013.03.005Smith, L. D., & Sanchez, S. (2003). Assessment of business potential at retail sites: empirical findings from a US supermarket chain. The International Review of Retail, Distribution and Consumer Research, 13(1), 37-58. doi:10.1080/0959396032000051684Suárez-Vega, R., Gutiérrez-Acuña, J. L., & Rodríguez-Díaz, M. (2014). Locating a supermarket using a locally calibrated Huff model. International Journal of Geographical Information Science, 29(2), 217-233. doi:10.1080/13658816.2014.958154Vigneau, E., Charles, M., & Chen, M. (2014). External preference segmentation with additional information on consumers: A case study on apples. Food Quality and Preference, 32, 83-92. doi:10.1016/j.foodqual.2013.05.00

INTERNAL BENCHMARKING IN RETAILING WITH DEA AND GIS: THE CASE OF A LOYALTY-ORIENTED SUPERMARKET CHAIN

[EN] Data Envelopment Analysis (DEA) is a relative measure of efficiency applied to a set of decision units and is being used more and more frequently in the supermarket sector. Nonetheless, given how strongly the sector's financials depend on demand, companies need to combine this measurement with trade area information to best manage corporate efficiency. In this paper, the proposal consists of integrating DEA with a clearly articulated, structural typology so that supermarkets, based on their particular characteristics, can determine which variables are most critical for improving their efficiency. This methodology has been validated in the case of one of Spain's five largest supermarket chains. A principal component analysis and a classification analysis were carried out on a series of internal management variables from 61 locations for which DEA had been used to calculate efficiency and to which multiple trade area variables were added using GIS. Some of them are related to the loyalty scheme membership programme. These latter variables described the implantation of the loyalty scheme member programme and were revealed as key elements for the efficiency of the supermarket. This methodology provides marketing profiles that are more adapted to local circumstances, thus allowing companies to set better internal benchmarking objectives.The authors would like to thank the Consum-Universitat Politècnica de València Chair (Cátedra) for its collaboration in this study.Baviera-Puig, A.; Baviera, T.; Buitrago Vera, JM.; Escribá Pérez, C. (2020). INTERNAL BENCHMARKING IN RETAILING WITH DEA AND GIS: THE CASE OF A LOYALTY-ORIENTED SUPERMARKET CHAIN. Journal of Business Economics and Management. 21(4):1035-1057. https://doi.org/10.3846/jbem.2020.12393S10351057214Álvarez-Rodríguez, C., Martín-Gamboa, M., & Iribarren, D. (2019). Combined use of Data Envelopment Analysis and Life Cycle Assessment for operational and environmental benchmarking in the service sector: A case study of grocery stores. Science of The Total Environment, 667, 799-808. doi:10.1016/j.scitotenv.2019.02.433APPLEBAUM, W., & COHEN, S. B. (1961). THE DYNAMICS OF STORE TRADING AREAS AND MARKET EQUILIBRIUM1. Annals of the Association of American Geographers, 51(1), 73-101. doi:10.1111/j.1467-8306.1961.tb00369.xBanker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078-1092. doi:10.1287/mnsc.30.9.1078Pestana Barros, C. (2006). Efficiency measurement among hypermarkets and supermarkets and the identification of the efficiency drivers. International Journal of Retail & Distribution Management, 34(2), 135-154. doi:10.1108/09590550610649795Pestana Barros, C., & Alves, C. A. (2003). Hypermarket retail store efficiency in Portugal. International Journal of Retail & Distribution Management, 31(11), 549-560. doi:10.1108/09590550310503285Barros, C. P., & Alves, C. (2004). An empirical analysis of productivity growth in a Portuguese retail chain using Malmquist productivity index. Journal of Retailing and Consumer Services, 11(5), 269-278. doi:10.1016/s0969-6989(03)00053-5BAVIERA-PUIG, A., BUITRAGO-VERA, J., & ESCRIBA-PEREZ, C. (2016). GEOMARKETING MODELS IN SUPERMARKET LOCATION STRATEGIES. Journal of Business Economics and Management, 17(6), 1205-1221. doi:10.3846/16111699.2015.1113198Brønn, C., & Brønn, P. S. (2005). Reputation and Organizational Efficiency: A Data Envelopment Analysis Study. Corporate Reputation Review, 8(1), 45-58. doi:10.1057/palgrave.crr.1540238Byrom, J., Hernández, T., Bennison, D., & Hooper, P. (2001). Exploring the geographical dimension in loyalty card data. Marketing Intelligence & Planning, 19(3), 162-170. doi:10.1108/02634500110391708CACHERO-MARTÍNEZ, S., & VÁZQUEZ-CASIELLES, R. (2017). LIVING POSITIVE EXPERIENCES IN STORE: HOW IT INFLUENCES SHOPPING EXPERIENCE VALUE AND SATISFACTION? Journal of Business Economics and Management, 18(3), 537-553. doi:10.3846/16111699.2017.1292311Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. doi:10.1016/0377-2217(78)90138-8Chaudhuri, M., Voorhees, C. M., & Beck, J. M. (2019). The effects of loyalty program introduction and design on short- and long-term sales and gross profits. Journal of the Academy of Marketing Science, 47(4), 640-658. doi:10.1007/s11747-019-00652-yDe Jorge Moreno, J. (2006). Regional regulation analysis of performance in Spanish retailing. International Journal of Retail & Distribution Management, 34(10), 773-793. doi:10.1108/09590550610691356Donthu, N., & Yoo, B. (1998). Retail productivity assessment using data envelopment analysis. Journal of Retailing, 74(1), 89-105. doi:10.1016/s0022-4359(99)80089-xDonthu, N., Hershberger, E. K., & Osmonbekov, T. (2005). Benchmarking marketing productivity using data envelopment analysis. Journal of Business Research, 58(11), 1474-1482. doi:10.1016/j.jbusres.2004.05.007Drèze, X., & Nunes, J. C. (2009). Feeling Superior: The Impact of Loyalty Program Structure on Consumers’ Perceptions of Status. Journal of Consumer Research, 35(6), 890-905. doi:10.1086/593946Emrouznejad, A., & Yang, G. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socio-Economic Planning Sciences, 61, 4-8. doi:10.1016/j.seps.2017.01.008Felício, J. A., & Villardón, M. P. G. (2015). FAMILY CHARACTERISTICS AND GOVERNANCE OF SMALL AND MEDIUM-SIZED FAMILY FIRMS. Journal of Business Economics and Management, 16(6), 1069-1084. doi:10.3846/16111699.2012.747446Gandhi, A., & Shankar, R. (2014). Efficiency measurement of Indian retailers using Data Envelopment Analysis. International Journal of Retail & Distribution Management, 42(6), 500-520. doi:10.1108/ijrdm-10-2012-0094Goss, J. (1995). «We Know Who You Are and We Know Where You Live»: The Instrumental Rationality of Geodemographic Systems. Economic Geography, 71(2), 171. doi:10.2307/144357Grewal, D., Levy, M., Mehrotra, A., & Sharma, A. (1999). Planning merchandising decisions to account for regional and product assortment differences. Journal of Retailing, 75(3), 405-424. doi:10.1016/s0022-4359(99)00015-9Hanousek, J., Kočenda, E., & Shamshur, A. (2015). Corporate efficiency in Europe. Journal of Corporate Finance, 32, 24-40. doi:10.1016/j.jcorpfin.2015.03.003HERRANZ, R. E., ESTÉVEZ, P. G., OLIVA, M. A. de V. y, & DÉ, R. (2017). LEVERAGING FINANCIAL MANAGEMENT PERFORMANCE OF THE SPANISH AEROSPACE MANUFACTURING VALUE CHAIN. Journal of Business Economics and Management, 18(5), 1005-1022. doi:10.3846/16111699.2017.1357655Kang, J., Alejandro, T. B., & Groza, M. D. (2015). Customer–company identification and the effectiveness of loyalty programs. Journal of Business Research, 68(2), 464-471. doi:10.1016/j.jbusres.2014.06.002Kim, H.-Y., Lee, J. Y., Choi, D., Wu, J., & Johnson, K. K. P. (2013). Perceived Benefits of Retail Loyalty Programs: Their Effects on Program Loyalty and Customer Loyalty. Journal of Relationship Marketing, 12(2), 95-113. doi:10.1080/15332667.2013.794100Kumar, V., & Petersen, J. A. (2005). Using a Customer-Level Marketing Strategy to Enhance Firm Performance: A Review of Theoretical and Empirical Evidence. Journal of the Academy of Marketing Science, 33(4), 504-519. doi:10.1177/0092070305275857Kumar, V., & Shah, D. (2004). Building and sustaining profitable customer loyalty for the 21st century. Journal of Retailing, 80(4), 317-329. doi:10.1016/j.jretai.2004.10.007Lao, Y., & Liu, L. (2009). Performance evaluation of bus lines with data envelopment analysis and geographic information systems. Computers, Environment and Urban Systems, 33(4), 247-255. doi:10.1016/j.compenvurbsys.2009.01.005Leenheer, J., van Heerde, H. J., Bijmolt, T. H. A., & Smidts, A. (2007). Do loyalty programs really enhance behavioral loyalty? An empirical analysis accounting for self-selecting members. International Journal of Research in Marketing, 24(1), 31-47. doi:10.1016/j.ijresmar.2006.10.005Lewis, H. F., & Sexton, T. R. (2004). Data Envelopment Analysis with Reverse Inputs and Outputs. Journal of Productivity Analysis, 21(2), 113-132. doi:10.1023/b:prod.0000016868.69586.b4Li, Y., & Liu, L. (2012). Assessing the impact of retail location on store performance: A comparison of Wal-Mart and Kmart stores in Cincinnati. Applied Geography, 32(2), 591-600. doi:10.1016/j.apgeog.2011.07.006López Roldán, P. (1996). La construcción de tipologías: metodología de análisis. Papers. Revista de Sociologia, 48, 9. doi:10.5565/rev/papers.1811López-Roldán, P., & Fachelli, S. (2015). Análisis de clasificación. In P. López-Roldán & S. Fachelli (Eds.), Metodología de la Investigación Social Cuantitativa (1a edición, versión 2). Bellaterra: Dipòsit Digital de Documents, Universitat Autónoma de Barcelona. http://ddd.uab.cat/record/142929López-Roldán, P., & Fachelli, S. (2016). Análisis factorial. In P. López-Roldán & S. Fachelli (Eds.), Metodología de la Investigación Social Cuantitativa (1a edición, versión 3). Bellaterra: Dipòsit Digital de Documents, Universitat Autónoma de Barcelona. http://ddd.uab.cat/record/142928Meyer-Waarden, L. (2007). The effects of loyalty programs on customer lifetime duration and share of wallet. Journal of Retailing, 83(2), 223-236. doi:10.1016/j.jretai.2007.01.002Meyer‐Waarden, L. (2008). The influence of loyalty programme membership on customer purchase behaviour. European Journal of Marketing, 42(1/2), 87-114. doi:10.1108/03090560810840925Meyer-Waarden, L., & Benavent, C. (2006). The Impact of Loyalty Programmes on Repeat Purchase Behaviour. Journal of Marketing Management, 22(1-2), 61-88. doi:10.1362/026725706776022308Mimouni-Chaabane, A., & Volle, P. (2010). Perceived benefits of loyalty programs: Scale development and implications for relational strategies. Journal of Business Research, 63(1), 32-37. doi:10.1016/j.jbusres.2009.01.008De Jorge Moreno, J. (2010). Productivity growth of European retailers: a benchmarking approach. Journal of Economic Studies, 37(3), 288-313. doi:10.1108/01443581011061285Mostafa, M. M. (2009). Benchmarking the US specialty retailers and food consumer stores using data envelopment analysis. International Journal of Retail & Distribution Management, 37(8), 661-679. doi:10.1108/09590550910966178Neves Bezerra de Melo, F. L., Sampaio, R. M. B., & Sampaio, L. M. B. (2018). Efficiency, productivity gains, and the size of Brazilian supermarkets. International Journal of Production Economics, 197, 99-111. doi:10.1016/j.ijpe.2017.12.016Nitkiewicz, T., Pachura, P., & Reid, N. (2014). An appraisal of regional intellectual capital performance using Data Envelopment Analysis. Applied Geography, 53, 246-257. doi:10.1016/j.apgeog.2014.06.011Noordhoff, C., Pauwels, P., & Odekerken‐Schröder, G. (2004). The effect of customer card programs. International Journal of Service Industry Management, 15(4), 351-364. doi:10.1108/09564230410552040Pantano, E., Priporas, C. V., & Dennis, C. (2018). A new approach to retailing for successful competition in the new smart scenario. International Journal of Retail & Distribution Management, 46(3), 264-282. doi:10.1108/ijrdm-04-2017-0080Patel, G. N., & Pande, S. (2012). Measuring the performance of pharmacy stores using discretionary and non-discretionary variables. OPSEARCH, 50(1), 25-41. doi:10.1007/s12597-012-0095-0Roig-Tierno, N., Baviera-Puig, A., Buitrago-Vera, J., & Escriba-Perez, C. (2018). Assessing food retail competitors with a multi-criteria GIS-based method. Economía Agraria y Recursos Naturales, 18(1), 5. doi:10.7201/earn.2018.01.01Roig-Tierno, N., Baviera-Puig, A., Buitrago-Vera, J., & Mas-Verdu, F. (2013). The retail site location decision process using GIS and the analytical hierarchy process. Applied Geography, 40, 191-198. doi:10.1016/j.apgeog.2013.03.005Sanjuan, N., Ribal, J., Clemente, G., & Fenollosa, M. L. (2011). Measuring and Improving Eco-efficiency Using Data Envelopment Analysis. Journal of Industrial Ecology, 15(4), 614-628. doi:10.1111/j.1530-9290.2011.00347.xSellers‐Rubio, R., & Mas‐Ruiz, F. (2006). Economic efficiency in supermarkets: evidences in Spain. International Journal of Retail & Distribution Management, 34(2), 155-171. doi:10.1108/09590550610649803Sellers‐Rubio, R., & Más‐Ruiz, F. J. (2009). Technical efficiency in the retail food industry. European Journal of Marketing, 43(5/6), 652-669. doi:10.1108/03090560910946981Steinhoff, L., & Palmatier, R. W. (2014). Understanding loyalty program effectiveness: managing target and bystander effects. Journal of the Academy of Marketing Science, 44(1), 88-107. doi:10.1007/s11747-014-0405-6Keh, H. T., & Chu, S. (2003). Retail productivity and scale economies at the firm level: a DEA approach. Omega, 31(2), 75-82. doi:10.1016/s0305-0483(02)00097-xThomas, R. R., Barr, R. S., Cron, W. L., & Slocum, J. W. (1998). A process for evaluating retail store efficiency: a restricted DEA approach. International Journal of Research in Marketing, 15(5), 487-503. doi:10.1016/s0167-8116(98)00021-4Uyar, A., Bayyurt, N., Dilber, M., & Karaca, V. (2013). Evaluating operational efficiency of a bookshop chain in Turkey and identifying efficiency drivers. International Journal of Retail & Distribution Management, 41(5), 331-347. doi:10.1108/ijrdm-01-2012-0001Vyt, D., & Cliquet, G. (2017). Towards a fairer manager performance measure: a DEA application in the retail industry. The International Review of Retail, Distribution and Consumer Research, 27(5), 450-467. doi:10.1080/09593969.2017.1383293Yoo, B., Donthu, N., & Pilling, B. K. (1998). Channel Efficiency. Journal of Marketing Channels, 6(3-4), 1-15. doi:10.1300/j049v06n03_01Yu, W., & Ramanathan, R. (2008). An assessment of operational efficiencies in the UK retail sector. International Journal of Retail & Distribution Management, 36(11), 861-882. doi:10.1108/09590550810911656Zheka, V. (2005). Corporate governance, ownership structure and corporate efficiency: the case of Ukraine. Managerial and Decision Economics, 26(7), 451-460. doi:10.1002/mde.125

Assessment of Irrigation Water Use Efficiency in Citrus Orchards Using AHP

[EN] Irrigation water use efficiency, the small size of the orchards, and part-time farmers are major issues for Spanish citriculture. How should irrigation water use efficiency be assessed? Does irrigation water use efficiency improve when increasing the size of the orchards? Are full-time farmers more efficient in irrigation water use than part-time ones? To address these three questions, we propose to apply a new multicriteria approach based on the analytic hierarchy process (AHP) technique and the participation of a group of experts. A new synthetic irrigation efficiency index (IEI) was proposed and tested using data from an irrigation community (IC) and a cooperative of farmers in the East of Spain. The results showed that the size of the orchards had no relation with the IEI scoring but full-time farmers tended to have better IEI scores and, thus, were more efficient. These results were obtained from a sample of 24 orchards of oranges, navelina variety, growing in a very similar environment, and agronomical characteristics. The proposed methodology can be a useful benchmarking tool for improving the irrigation water management in other ICs taking into account the issues related to farm data sharing recorded during the case study.The APC was funded by the Project 2019ES06RDEI7346 Improving the use of water and energy in modernized irrigation of fruit trees (GO InnoWater), funded by the Spanish Rural Development Program (2014-2020): EAFRD and MAPA.Poveda Bautista, R.; Roig-Merino, B.; Puerto, H.; Buitrago Vera, JM. (2021). Assessment of Irrigation Water Use Efficiency in Citrus Orchards Using AHP. International Journal of Environmental research and Public Health (Online). 18(11):1-14. https://doi.org/10.3390/ijerph18115667S114181

Consumer profile analysis for different types of meat in Spain

[EN] It is important to analyse the consumer profile of each type of meat to better adapt the marketing mix to each one. To this end, we examined the average consumption frequency of different types of meat based on two methodologies: consumer segmentation using the food-related lifestyle (FRL) framework, giving rise to 4 segments, and analysis of socio-demographic profiles. The variables used were: sex, age, educational level, social class, number of people in the household, presence of children younger than 18 in the home, geographical area and habitual residence. Beef was the only meat type significant in both analyses. Turkey meat only appeared as significant in the FRL analysis. The other meats (chicken, pork, rabbit and lamb) were only significant in the sociodemographic variables analysis. From the outcomes we may conclude that there is no single consumer profile, which rather depends on the type of meat.Escribá Pérez, C.; Baviera-Puig, A.; Buitrago Vera, JM.; Montero De Vicente, L. (2017). Consumer profile analysis for different types of meat in Spain. Meat Science. 129:120-126. doi:10.1016/j.meatsci.2017.02.015S12012612

Analysis of chicken and turkey meat consumption by segmentation of Spanish consumers using food-related lifestyle

[EN] Aim of study: Commercially, chicken meat has a similar positioning to turkey meat, as both are healthy and low-fat meats. For this reason, we proposed analysing consumer behaviour with respect to each of these meats based on market segmentation. Area of study: Spain. Material and methods: We carried out a telephone survey with an error of ± 4.0% at a confidence level of 95.5%, using the food-related lifestyle (FRL) instrument as part of the questionnaire. The statistical analysis techniques employed were different depending on the objective pursued: univariate, bivariate and multivariate analysis. Main results: Five segments were obtained: ¿Manager cook¿ (24.5%), ¿Healthy cook¿ (20.8%), ¿Concerned with food, but not cooks¿ (22%), ¿Total detachment¿ (11.9%) and ¿Rational shopper with little interest in cuisine¿ (20.8%). Notwithstanding the similar positioning of chicken and turkey meats, there are significant differences in purchasing and consumption habits between FRL segments. Specifically, there were significant differences in the frequency of purchase, the usual shopping location, purchasing criteria and preparation methods. Research highlights: Knowing the profile of these segments allows us to adapt the marketing mix (product, place, price and promotion) to each one. This is very useful for the companies due to the wide demand they face. First, they can choose the FRL segments to target and, second, they can define an appropriate marketing strategy according to these segments. In this way, market segmentation strategy based on food-related lifestyles may ensure companies a greater likelihood of success in the market.Autonomous Government of the Valencian Region, Spain: AICO/2017/066 (Project ¿Sustainability of the Food Value Chain: From Production to Responsible Consumption¿)Baviera-Puig, A.; Montero De Vicente, L.; Escribá-Pérez, C.; Buitrago Vera, JM. (2021). Analysis of chicken and turkey meat consumption by segmentation of Spanish consumers using food-related lifestyle. Spanish Journal of Agricultural Research (Online). 19(1):1-16. https://doi.org/10.5424/sjar/2021191-16419S11619

Answers to the consumer confusion for information ambiguous: the case of the cosmetics of a supermarkets chain in Spain

[ES] Este estudio expuso y agrupó las reacciones de los consumidores ante la confusión generada por información ambigua para una marca de cosméticos de una cadena de supermercados. El método de recogida de la información fue la netnografía, usando para su análisis Componentes Principales Categóricos. Este trabajo supuso una aportación a la literatura sobre la confusión del consumidor porque la información se recogió a partir de comentarios on-line. La respuesta más citada fue la mala calidad percibida del producto. En relación al marco teórico, hay dos reacciones que no habían sido señaladas: las críticas a los medios de comunicación y la posible hostilidad de la competencia. La agrupación de las respuestas dadas se ha centrado fundamentalmente en dos aspectos: la imagen percibida de las empresas y la calidad de los productos.[EN] This study explained and grouped the reactions of consumers to the confusion generated by ambiguous information for a cosmetic brand in supermarket chain. The method of collecting information was netnography, using Categorical Principal Component analysis. This work represents a contribution to the literature on consumer confusion because the information was collected from on-line comments. The most cited answer was perceived poor quality of the product. In relation to the theoretical framework, there are two reactions that were not mentioned: the criticism of the media and possible hostility from the competition. The grouping of answers has focused primarily on two issues: the perceived business image and product quality.Clemente-Ricolfe, J.; Escribá-Pérez, C.; Buitrago Vera, JM. (2014). Respuestas ante la confusión del consumidor por información ambigua: el caso de los cosméticos de una cadena de supermercados en España. Aposta. (62):1-25. http://hdl.handle.net/10251/2009841256

Un modelo de geomarketing para la localización de supermercados: diseño y aplicación práctica

El geomarketing es una disciplina en plena evolución gracias a sus múltiples aplicaciones en el mundo empresarial. En este documento de trabajo, utilizando como base el modelo MCI de la teoría de la localización, se desarrolla un modelo de geomarketing con el fin de contribuir a la estrategia de localización de los supermercados. Los principales resultados obtenidos pueden clasificarse en dos grandes grupos: conceptuales y metodológicos. Por un lado, se profundiza en el conocimiento de los atributos determinantes de la atracción comercial de un supermercado y, por otro lado, la incorporación de los Sistemas de Información Geográfica facilita enormemente su aplicación práctica.Baviera Puig, MA.; Buitrago Vera, JM.; Rodríguez Barrio, JE. (2013). Un modelo de geomarketing para la localización de supermercados: diseño y aplicación práctica. Documentos de Trabajo de la Cátedra Fundación Ramón Areces de Distribución Comercial (DOCFRADIS). (1):1-27. http://hdl.handle.net/10251/60788S127

Importance of the geographic origin in the agri-food products consumption

[ES] En este trabajo se pretende determinar la importancia del origen geográfico en el consumo de productos agroalimentarios, analizando la preferencia entre alimentos autóctonos y foráneos. El mapa de posicionamiento de los productos agroalimentarios según su origen geográfico, refleja que los de la Comunidad Valenciana y el resto de España, se perciben similares. En cambio, los productos agroalimentarios de Francia y Marruecos destacan por innovación, y precio bajo, respectivamente. También se cuantifica la existencia de dos segmentos en el mercado agroalimentario, el ¿Etnocentrista¿ que representa un 60%, y el ¿No Etnocentrista¿.[EN] In this paper pretend determine the importance of the geographic origin in the agri-food product consumption, analysing the preference between native and foreign foods. The map of positioning of agri-food products according geographic origin, reflects that the Comunidad Valenciana and the rest of Spain, perceive similars. However, agri-food products of France and Morocco emphasize by innovation, and low price, respectively. Also, quantifies the existence of two segments in the agrifood market, the ¿Ethnocentrist¿ that a 60% represent, and the ¿Non Ethnocentrist¿.Clemente Ricolfe, JS.; Rodríguez Barrio, JE.; Buitrago Vera, JM. (2011). Importancia del origen geográfico en el consumo de productos agroalimentarios. Scripta Nova-Revista Electronica de Geografia y Ciencias Sociales. XV(369):1-10. http://hdl.handle.net/10251/30017S110XV36

Análisis metodológico del modelo de interacción espacial MCI

En la actualidad, la distribución comercial minorista se enfrenta a nuevos y crecientes retos. En estas empresas, la localización del establecimiento es crucial en su planificación comercial. De hecho, una estrategia de localización óptima puede llegar a convertirse en el elemento diferenciador frente a la competencia. Por este motivo, partiendo de un modelo basado en la teoría de la interacción espacial, se proponen mejoras del mismo desde un punto de vista metodológico. Los avances conseguidos son de tres tipos: 1) Método de calibración; 2) Ampliación del modelo integrando la organización espacial de las alternativas; 3) Valoración de la segmentación.Baviera Puig, MA.; Buitrago Vera, JM.; Rodríguez Barrio, JE. (2012). Análisis metodológico del modelo de interacción espacial MCI. Documentos de Trabajo de la Cátedra Fundación Ramón Areces de Distribución Comercial (DOCFRADIS). (8):1-22. http://hdl.handle.net/10251/60787122