Design and psychometric validation of a Customer Analytics Capabilities (CAC) scale: empirical evidence in Colombian organizations
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A pesar de que la medición de las capacidades de analítica del cliente (CAC) ha venido despertando interés entre académicos y empresarios, se carece de un instrumento que sintetice y describa las principales rutinas organizativas implicadas en tal constructo, sobre la base de manifestaciones empíricas aportadas por la literatura científica. El estudio busca aportar al cierre de esta brecha, mediante el diseño y validación psicométrica de un modelo de medida de las CAC. La muestra comprende datos de encuestas de 101 empresas colombianas; la fuente de información corresponde a profesionales de áreas de mercadeo o analítica. Se utiliza un marco de analítica psicométrica, el cual incorpora análisis factorial exploratorio y confirmatorio. Se obtienen dos modelos de medida plausibles: uno unidimensional y otro tridimensional. El tridimensional consta de 10 ítems agrupados en los factores: capacidad para la analítica de captura de clientes, capacidad para la analítica del sostenimiento de clientes, y capacidad para la analítica de la evaluación económica de clientes. Éste satisface criterios de ajuste, validez de contenido, validez convergente y discriminante, fiabilidad y equidad (examinando área, cargo e infraestructura para analítica) y es útil cuando se desea profundizar en las dimensiones que conforman las CAC. El modelo unidimensional contiene 14 ítems, también presenta calidad psicométrica y es útil cuando se desea una aproximación parsimoniosa al atributo general de las CAC, sin requerir profundización o segmentación según dimensiones. Las escalas desarrolladas hacen medibles las CAC, a partir de un conjunto de rutinas que reconfiguran capacidades operacionales tradicionales en mercadeo. A su vez, facilitan la ejecución de diagnósticos organizativos confiables y la definición de agendas de trabajo para departamentos de analítica. Igualmente, propician futuros trabajos de relacionamiento entre las CAC y el desempeño empresarial.
AERA, APA, & NCME. (1999). The Standards for Educational and Psychological Testing. AERA Publications Sales. https://www.scirp.org/reference/ReferencesPapers?ReferenceID=1779513
Boldosova, V. (2019). Telling stories that sell: The role of storytelling and big data analytics in smart service sales. Industrial Marketing Management, 86, 122-134. https://doi.org/10.1016/j.indmarman.2019.12.004 DOI: https://doi.org/10.1016/j.indmarman.2019.12.004
Cao, G., Tian, N. (2020). Enhancing customer-linking marketing capabilities using marketing analytics. Journal of Business & Industrial Marketing, 35(7), 1289-1299. https://doi.org/10.1108/JBIM-09-2019-0407 DOI: https://doi.org/10.1108/JBIM-09-2019-0407
Carbone, L., Haeckel, S. (1994). Engineering customer experience. Marketing Management, 3(3), 8-19. https://www.researchgate.net/publication/265031917_Engineering_Customer_Experiences
Dinh, T. L., Vu, T. M., Dam, N. A., & Nguyen, C.N. (July, 2022). Trivi: A Conceptual Framework for Customer Intelligence Systems for Small and Medium-sized Enterprises. Conference: Pacific Asia Conference on Information Systems (PACIS). Association for Information Systems, Taipei, Australia. https://www.researchgate.net/publication/361613382_Trivi_A_Conceptual_Framework_for_Customer_Intelligence_Systems_for_Small_and_Medium-_sized_Enterprises
Dubey, R., Gunasekaran, A., & Childe, S. (2018). Big data analytics capability in supply chain agility: The moderating effect of organizational flexibility. Management Decision, 57(8), 1-38, https://doi.org/10.1108/MD-01-2018-0119 DOI: https://doi.org/10.1108/MD-01-2018-0119
Ediger, D., Appling, S., Briscoe, E., McColl, R., & Poovey, J. (9-11 September 2014). Real-time streaming intelligence: Integrating graph and NLP analytics. IEEE High Performance Extreme Computing Conference (HPEC), Waltham, USA. https://ieeexplore.ieee.org/document/7040990 DOI: https://doi.org/10.1109/HPEC.2014.7040990
Egaña, M. , Barrios S., Núñez M., & Camus, M. (2014). Optimal methods for content validity. Revista Cubana de Educación Medica Superior, 28(3), 547-558. https://www.researchgate.net/publication/282374966_Optimal_method_for_content_validity
Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897-904. https://doi.org/10.1016/j.jbusres.2015.07.001 DOI: https://doi.org/10.1016/j.jbusres.2015.07.001
Fernández, A. (2019). Artificial intelligence in financial services. Economic Bulletin, 2. https://repositorio.bde.es/handle/123456789/9047 DOI: https://doi.org/10.2139/ssrn.3366846
Fox-Rushby, J., Cairns, J. (2005). Economic Evaluation. Open University Press.
Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S., Childe, S., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308-317. https://doi.org/10.1016/j.jbusres.2016.08.004 DOI: https://doi.org/10.1016/j.jbusres.2016.08.004
Hallikainen, H., Savimäki, E., & Laukkanen, T. (2020). Fostering B2B sales with customer big data analytics. Industrial Marketing Management, 86, 90-98. http://dx.doi.org/10.1016/j.indmarman.2019.12.005 DOI: https://doi.org/10.1016/j.indmarman.2019.12.005
Hanaysha, J. R., Shaikh, M. E., & Alzoubi, H. M. (2021). Importance of Marketing Mix Elements in Determining Consumer Purchase Decision in the Retail Market. International Journal of Service Science, Management, Engineering, and Technology, 12(6). https://doi.org/10.4018/IJSSMET.2021110104 DOI: https://doi.org/10.4018/IJSSMET.2021110104
Hays, R.D., Schalet, B.D., Spritzer, K.L., & Cella, D. (2017). Two-item PROMIS® global physical and mental health scales. Journal of Patient-Reported Outcomes, 1(1). https://doi.org/10.1186/s41687-017-0003-8 DOI: https://doi.org/10.1186/s41687-017-0003-8
He, W., Tian, X., & Wang, F.-K. (2019). Innovating the customer loyalty program with social media: A case study of best practices using analytics tools. Journal of Enterprise Information Management, 32(5). https://doi.org/10.1108/JEIM-10-2018-0224 DOI: https://doi.org/10.1108/JEIM-10-2018-0224
Hinkin, T. R. (1995). A review of scale development practices in the study of organizations. Journal of Management, 21(5), 967-988. https://doi.org/10.1016/0149-2063(95)90050-0 DOI: https://doi.org/10.1016/0149-2063(95)90050-0
Holland, C. P., Thornton, S. C., & Naudé, P. (2019). B2B analytics in the airline market: Harnessing the power of consumer big data. Industrial Marketing Management, 86, 52-64. https://doi.org/10.1016/j.indmarman.2019.11.002 DOI: https://doi.org/10.1016/j.indmarman.2019.11.002
Hossain, M. A., Akter, S., & Yanamandram, V. (2020a). Revisiting customer analytics capability for data-driven retailing. Journal of Retailing and Consumer Services, 56. https://doi.org/10.1016/j.jretconser.2020.102187 DOI: https://doi.org/10.1016/j.jretconser.2020.102187
Hossain, M. A., Akter, S., & Yanamandram, V. K. (2020b). Customer Analytics Capabilities in the Big Data Spectrum: A Systematic Approach to Achieve Sustainable Firm Performance. Sydney Business School Papers, 1-17. https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1616&context=gsbpapers
Kolsarici, C., Vakratsas, D., & Naik, P. A. (2020). The Anatomy of the Advertising Budget Decision: How Analytics and Heuristics Drive Sales Performance. Journal of Marketing Research, 57(3). https://doi.org/10.1177/0022243720907578 DOI: https://doi.org/10.1177/0022243720907578
Le, T. M., Liaw, S.-y., & Bui, M.-T. (2020). The role of perceived risk and trust propensity in the relationship between negative perceptions of applying big data analytics and consumers’ responses. WSEAS Transactions on Business and Economics, 17, 426-435. http://dx.doi.org/10.37394/23207.2020.17.41 DOI: https://doi.org/10.37394/23207.2020.17.41
Liao, S.-H., Hsu, S.-Y. (2020). Big data analytics for investigating Taiwan Line sticker social media marketing. Asia Pacific Journal of Marketing and Logistics, 32(2), 589-606. https://doi.org/10.1108/APJML-03-2019-0211 DOI: https://doi.org/10.1108/APJML-03-2019-0211
Louro, A., Brandrão, M. M., Jaklič, J., & Sarcinelli, A. (2019). How can Customer Analytics Capabilities influence organizational performance? A moderated mediation analysis. Brazlian Business Review, 16(4). https://doi.org/10.15728/bbr.2019.16.4.4 DOI: https://doi.org/10.15728/bbr.2019.16.4.4
Madhavan, R., Shah, R. H., & Grover, R. (1994). Motivations for and theoretical foundations of relationship marketing (pp. 183-190). In AMA Winter Educators’ Conference , Chicago, USA.
Mariani, M. M., Wamba, S. F. (2020). Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies. Journal of Business Research, 121, 338-352. https://doi.org/10.1016/j.jbusres.2020.09.012 DOI: https://doi.org/10.1016/j.jbusres.2020.09.012
Martínez, M., Hernández, M., & Hernández, M. (2006). Psychometry. Editorial Alliance. https://dialnet.unirioja.es/servlet/libro?codigo=284004
Maya-Restrepo, M. A. (2023). Design, development and psychometric validation of a measurement model of the client’s analytical capabilities under a theory/data guided approach. [Thesis Universidad de Antioquia].
Nethravathi, P. S., Bai, G. V., Spulbar, C., Suhan, M., Birau, R., Calugaru, T., Hawaldar, l. Thonse., & Ejaz, A. (2020). Business intelligence appraisal based on customer behavior profile by using hobby based opinion mining in India: a case study. Economic Research, 33(1). https://doi.org/10.1080/1331677X.2020.1763822 DOI: https://doi.org/10.1080/1331677X.2020.1763822
Nicholas, M. K., McGuire, B. E., & Asghari, A. (2015). A 2-item short form of the Pain Self-efficacy Questionnaire: development and psychometric evaluation of PSEQ-2. Journal of Pain, 16(2), 153-163. https://doi.org/10.1016/j.jpain.2014.11.002 DOI: https://doi.org/10.1016/j.jpain.2014.11.002
O’Neill, M., Brabazon, A. (2019). Business analytics capability, organizational value and competitive advantage. Journal of Business Analytics, 2, 160-173. https://doi.org/10.1080/2573234X.2019.1649991 DOI: https://doi.org/10.1080/2573234X.2019.1649991
Pavlou, P. A., Sawy, O. A. (2011). Understanding the Elusive Black Box of Dynamic Capabilities. Decision Sciences, 42(1), 239-273. https://doi.org/10.1111/j.1540-5915.2010.00287.x DOI: https://doi.org/10.1111/j.1540-5915.2010.00287.x
Pérez-Rave, J. I. (2021a). Data/Text Mining – Structural – Multicriteria as a strategic resource in personnel selection. https://repositorio.unal.edu.co/bitstream/handle/unal/80145/71225056.2021.pdf?sequence=2
Pérez-Rave, J. I. (2021b). MinerConstructo: intelligent framework to learn, update and practice construct mining with scientific rigor (pp. 10-986-55). https://www.idinnov.com/terminos-y-condiciones-idinnov-s-a-s/
Pérez-Rave, J. I., Figueroa, G. A., & Echavarría, F.G. (2022). A scale for measuring healthcare service quality incorporating patient-centered care and using a psychometric analytics framework. Journal of Health Organization and Management, 36(6). https://doi.org/10.1108/JHOM-10-2021-0387 DOI: https://doi.org/10.1108/JHOM-10-2021-0387
Pérez-Rave, J. I., González-Echavarría, F., & Correa-Morales, J. C. (2022). A measure of dignified treatment for healthcare workers: design and psychometric properties. Behaviormetrika, 50, 287-316. https://link.springer.com/article/10.1007/s41237-022-00180-0 DOI: https://doi.org/10.1007/s41237-022-00180-0
Pérez-Rave, J., Guerrero, R. F., Vallina, A., & González-Echavarría, F. (2022). A measurement model of dynamic capabilities of the continuous improvement project and its role in the renewal of the company’s products/services. Operations Management Research, 16. https://link.springer.com/article/10.1007/s12063-022-00281-9 DOI: https://doi.org/10.1007/s12063-022-00281-9
Petrescu, M., Krishen, A., & Bui, M. (2020). The internet of everything: implications of marketing analytics from a consumer policy perspective. Journal of Consumer Marketing, 37(6). https://doi.org/10.1108/JCM-02-2019-3080 DOI: https://doi.org/10.1108/JCM-02-2019-3080
Pine, B., Gilmore, J. (1998). Welcome to the economic experience. Harvard Business Review, 78(1), 97-105. https://hbr.org/1998/07/welcome-to-the-experience-economy
Ployhart, R. E., Schneider, B. (2012). The social and organizational context of personnel selection. In N. Schmitt (Ed.) , Oxford Library of Psychology, The Oxford handbook of personnel assessment and selection (pp. 48-67). https://doi.org/10.1093/oxfordhb/9780199732579.013.0004 DOI: https://doi.org/10.1093/oxfordhb/9780199732579.013.0004
Ramana, A. V., Rao, A. S., & Reddy, E. K. (2019). Applications of business intelligence and decision making for the customer behaviour analysis in telecom industry. International Journal of Recent Technology and Engineering, 7. https://www.ijrte.org/wp-content/uploads/papers/v7i6s2/F11150476S219.pdf
Real Academia Española. (s.f.). Diccionario de la lengua española, 23° ed. [versión 23.7 en línea]. https://dle.rae.es
Rajan, A. P. (2019). The effectiveness of social media content marketing towards brand health of a company: Social media analytics. International Journal of Scientific and Technology Research, 8(11). http://www.ijstr.org/final-print/nov2019/The-Effectiveness-Of-Social-Media-Content-Marketing-Towards-Brand-Health-Of-A-Company-Social-Media-Analytics.pdf
Rajendran, S. (2020). Improving the performance of global courier & delivery services industry by analyzing the voice of customers and employees using text analytics. International Journal of Logistics Research and Applications, 24(5). https://doi.org/10.1080/13675567.2020.1769042 DOI: https://doi.org/10.1080/13675567.2020.1769042
Rakhman, R. A., Widiastuti, R. Y., Legowo, N., & Kaburuan, E. R. (2019). Big data analytics implementation in banking industry - case study cross selling activity in indonesia’s commercial bank. International Journal of Scientific and Technology Research, 8(9). https://api.semanticscholar.org/CorpusID:204901775
Rezaei, G., Hosseini, S. M., & Sana, S. S. (2022). Exploring the Relationship between Data Analytics Capability and Competitive Advantage: The Mediating Roles of Supply Chain Resilience and Organization Flexibility. Sustainability, 14. https://ideas.repec.org/a/gam/jsusta/v14y2022i16p10444-d894732.html DOI: https://doi.org/10.3390/su141610444
Sackett, P. R., Putka, D. J., & McCloy, R. A. (2012). The Concept of Validity and the Process of Validation. In N. Schmitt (Ed.), The Oxford Handbook of Personnel Assessment and Selection (pp. 91-118). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199732579.013.0006 DOI: https://doi.org/10.1093/oxfordhb/9780199732579.013.0006
Slater, S. F. & Narver, J. C. (1999). Market-oriented is more than being customer-led. Strategic Management Journal, 20(12), 1165-1168. https://doi.org/10.1002/(SICI)1097-0266(199912)20:12<1165::AID-SMJ73>3.0.CO;2-%23 DOI: https://doi.org/10.1002/(SICI)1097-0266(199912)20:12<1165::AID-SMJ73>3.0.CO;2-#
Sohrabi, B., Vanani, I. R., Nikaein, N., & Kakavand, S. (2019). A predictive analytics of physicians prescription and pharmacies sales correlation using data mining. International Journal of Pharmaceutical and Healthcare Marketing, 13(2). https://doi.org/10.1108/IJPHM-11-2017-0066 DOI: https://doi.org/10.1108/IJPHM-11-2017-0066
Sumbaly, R., Kreps, J., & Shah, S. (junio de 2013). The “Big Data” Ecosystem at LinkedIn. https://doi.org/10.1145/2463676.2463707 DOI: https://doi.org/10.1145/2463676.2463707
Sun, N., Morris, J., Xu, J., Zhu, X., & Xie, M. (2014). iCARE: a framework for big data based banking customer analytics. IBM Journal of Research and Development, 58(5). https://ieeexplore.ieee.org/document/6964895 DOI: https://doi.org/10.1147/JRD.2014.2337118
Swanson, R. A., Holton, E. F. (2005). Research in Organizations: foundations and methods of inquiry. Barrett-Koehler Publishers. https://www.bkconnection.com/static/Research_in_Organizations_EXCERPT.pdf
Teece, D., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7%3C509::AID-SMJ882%3E3.0.CO;2-Z DOI: https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
Vecchio, P. D., Mele, G., Passiante, G., & Fanuli, D. V. (2020). Detecting customers knowledge from social media big data: toward an integrated methodological framework based on netnography and business analytics. Journal of Knowledge Management, 24(4). https://doi.org/10.1108/JKM-11-2019-0637 DOI: https://doi.org/10.1108/JKM-11-2019-0637
Wilder, C. R., Ozgur, C. O. (2015). Business Analytics Curriculum for Undergraduate Major. INFORMS Transactions on Education, 15(2), 180-187. https://pubsonline.informs.org/doi/10.1287/ited.2014.0134 DOI: https://doi.org/10.1287/ited.2014.0134
Yaghmale, F. (2003). Content validity and its estimation. Journal of Medical Education, 3. https://api.semanticscholar.org/CorpusID:73134288
Yerpude, S., Singhal, T. K. (2019). “Custolytics”: Internet of Things based customer analytics aiding customer engagement strategy in emerging markets – an empirical research. International Journal of Emerging Markets, 16(1). https://doi.org/10.1108/IJOEM-05-2018-0250 DOI: https://doi.org/10.1108/IJOEM-05-2018-0250
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Accepted 2024-04-29
Published 2024-03-18
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