Systematic literature review on customer analytics capabilities
Main Article Content
Although the concept of Customer Analytics Capabilities (CAC) has garnered interest among academics and business professionals, there remains a lack of consensus regarding its conceptualization and observable manifestations. This study conducts a systematic literature review on CAC to contribute to this topic, following the stages of locating (resulting in 42 studies), describing (synthesizing definitions and uses of analytics in customer-related domains, among others), deepening (identifying and interpreting common patterns in the studies), and disseminating (preparing the report). Various ways of understanding analytics in organizational customer contexts are discovered and analyzed using a reference conceptual model, synthesizing conceptions (action/method, complex process, or strategic resource) and roles (development of operational capabilities, dynamic capabilities, or strategy adjustment). Additionally, empirical manifestations corresponding to the different conceptions are identified (e.g., determining the effectiveness of specific campaigns based on data). Nine business profiles summarizing underlying maturity levels in CAC are generated from the combination of conceptions and roles. This study clarifies CAC and its observable manifestations based on evidence from the consolidation, standardization, and synthesis of relevant scientific literature on the subject. Therefore, it is useful for leaders of analytics areas in customer contexts and researchers who wish to have a comprehensive theoretical basis for developing future measurement scales.
Arroyave, F., Redondo, A., & Dasí, A. (2021). Student commitment to social responsibility: Systematic literature review, conceptual model, and instrument. Intangible Capital, 17(1), 52-72. https://doi.org/10.3926/ic.1685 DOI: https://doi.org/10.3926/ic.1685
Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377-386. https://doi.org/10.1162/qss_a_00019 DOI: https://doi.org/10.1162/qss_a_00019
Barrales‐Molina, V., Martínez‐López, F. J., & Gázquez‐Abad, J. C. (2014). Dynamic marketing capabilities: Toward an integrative framework. International Journal of Management Reviews, 16(4), 397-416. https://www.researchgate.net/publication/259550144_Dynamic_Marketing_Capabilities_Toward_an_Integrative_Framework DOI: https://doi.org/10.1111/ijmr.12026
Berggrun, L., Salamanca, J., Díaz, J., & Ospina, J. D. (2020). Profitability and money propagation in communities of bank clients: A visual analytics approach. Finance Research Letters, 37, 101387. https://doi.org/10.1016/j.frl.2019.101387 DOI: https://doi.org/10.1016/j.frl.2019.101387
Boldosova, V. (2020). 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
Bruni, D. S., Verona, G. (2009). Dynamic marketing capabilities in Science‐based firms: An exploratory investigation of the pharmaceutical industry. British Journal of management, 20, S101-S117. https://www.researchgate.net/publication/227658619_Dynamic_Marketing_Capabilities_in_Science-based_Firms_an_Exploratory_Investigation_of_the_Pharmaceutical_Industry DOI: https://doi.org/10.1111/j.1467-8551.2008.00615.x
Cañedo Andalia, R., Rodríguez Labrada, R., y Montejo Castells, M. (2010). Scopus: la mayor base de datos de literatura científica arbitrada al alcance de los países subdesarrollados. Acimed, 21(3), 270-282. http://scielo.sld.cu/scielo.php?pid=S1024-94352010000300002&script=sci_arttext
Cao, G., Tian, N. (2020). Enhancing customer-linking marketing capabilities using marketing analytics. Journal of Business & Industrial Marketing, 35(7), 1289-1299. https://www.emerald.com/insight/content/doi/10.1108/JBIM-09-2019-0407/full/html DOI: https://doi.org/10.1108/JBIM-09-2019-0407
Cao, G., Duan, Y., & El Banna, A. (2019). A dynamic capability view of marketing analytics: Evidence from UK firms. Industrial Marketing Management, 76, 72-83. https://doi.org/10.1016/j.indmarman.2018.08.002 DOI: https://doi.org/10.1016/j.indmarman.2018.08.002
Dam, N. A. K., Le Dinh, T., & Menvielle, W. (2021). Towards a conceptual framework for customer intelligence in the era of big data. International Journal of Intelligent Information Technologies, 17(4), 64-80. https://www.igi-global.com/article/towards-a-conceptual-framework-for-customer-intelligence-in-the-era-of-big-data/289968 DOI: https://doi.org/10.4018/IJIIT.289968
Day, G. S. (1994). The capabilities of market-driven organizations. Journal of Marketing, 58(4), 37-52. https://doi.org/10.1177/002224299405800404 DOI: https://doi.org/10.1177/002224299405800404
De Moya-Anegón, F., Chinchilla-Rodríguez, Z., Vargas-Quesada, B., Corera-Álvarez, E., Muñoz-Fernández, F. J., González-Molina, A., & Herrero-Solana, V. (2007). Coverage analysis of Scopus: A journal metric approach. Scientometrics, 73, 53-78. https://link.springer.com/article/10.1007/s11192-007-1681-4 DOI: https://doi.org/10.1007/s11192-007-1681-4
Del Vecchio, P., Mele, G., Passiante, G., Vrontis, D., & Fanuli, C. (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), 799-821. https://doi.org/10.1108/JKM-11-2019-0637 DOI: https://doi.org/10.1108/JKM-11-2019-0637
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
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
García-Cardona, A., León-Darder, F. (2023). Novel taxonomy of sustainability soft and hard practices in the food supply chain. International Journal of Logistics Research and Applications, 26(10), 1241-1266. https://doi.org/10.1080/13675567.2022.2038553 DOI: https://doi.org/10.1080/13675567.2022.2038553
González, F., del Val, M. P., & Cano, A. R. (2022). Systematic literature review of interpretative positions and potential sources of resistance to change in organizations. Intangible Capital, 18(2), 145-165. https://www.intangiblecapital.org/index.php/ic/article/view/1806 DOI: https://doi.org/10.3926/ic.1806
Hallikainen, H., Savimäki, E., & Laukkanen, T. (2020). Fostering B2B sales with customer big data analytics. Industrial Marketing Management, 86, 90-98. https://doi.org/10.1016/j.indmarman.2019.12.005 DOI: https://doi.org/10.1016/j.indmarman.2019.12.005
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), 807-823. https://doi.org/10.1108/JEIM-10-2018-0224 DOI: https://doi.org/10.1108/JEIM-10-2018-0224
He, W., Zhang, W., Tian, X., Tao, R., & Akula, V. (2019). Identifying customer knowledge on social media through data analytics. Journal of Enterprise Information Management, 32(1), 152-169. https://doi.org/10.1108/JEIM-02-2018-0031 DOI: https://doi.org/10.1108/JEIM-02-2018-0031
Holland, C. P., Thornton, S. C., & Naudé, P. (2020). 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. (2022). Customer Analytics Capabilities in the Big Data Spectrum: A Systematic Approach to Achieve Sustainable Firm Performance. In Research Anthology on Big Data Analytics, Architectures, and Applications (pp. 888-901). IGI Global. https://doi.org/10.4018/978-1-6684-3662-2.ch041 DOI: https://doi.org/10.4018/978-1-6684-3662-2.ch041
Hu, Y., Xu, A., Hong, Y., Gal, D., Sinha, V., & Akkiraju, R. (2019). Generating business intelligence through social media analytics: Measuring brand personality with consumer-, employee-, and firm-generated content. Journal of Management Information Systems, 36(3), 893-930. https://doi.org/10.1080/07421222.2019.1628908 DOI: https://doi.org/10.1080/07421222.2019.1628908
Ilk, N., Shang, G., & Goes, P. (2020). Improving customer routing in contact centers: An automated triage design based on text analytics. Journal of Operations Management, 66(5), 553-577. https://doi.org/10.1002/joom.1084 DOI: https://doi.org/10.1002/joom.1084
Jena, R. (2020). An empirical case study on Indian consumers’ sentiment towards electric vehicles: A big data analytics approach. Industrial Marketing Management, 90, 605-616. https://doi.org/10.1016/j.indmarman.2019.12.012 DOI: https://doi.org/10.1016/j.indmarman.2019.12.012
Kakatkar, C., Spann, M. (2019). Marketing analytics using anonymized and fragmented tracking data. International Journal of Research in Marketing, 36(1), 117-136. https://doi.org/10.1016/j.ijresmar.2018.10.001 DOI: https://doi.org/10.1016/j.ijresmar.2018.10.001
Kauffmann, E., Peral, J., Gil, D., Ferrández, A., Sellers, R., & Mora, H. (2020). A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making. Industrial Marketing Management, 90, 523-537. https://doi.org/10.1016/j.indmarman.2019.08.003 DOI: https://doi.org/10.1016/j.indmarman.2019.08.003
Ketzenberg, M. E., Abbey, J. D., Heim, G. R., & Kumar, S. (2020). Assessing customer return behaviors through data analytics. Journal of Operations Management, 66(6), 622-645. https://doi.org/10.1002/joom.1086 DOI: https://doi.org/10.1002/joom.1086
Khouja, M., Subramaniam, C., & Vasudev, V. (2020). A comparative analysis of marketing promotions and implications for data analytics. International Journal of Research in Marketing, 37(1), 151-174. https://doi.org/10.1016/j.ijresmar.2019.07.002 DOI: https://doi.org/10.1016/j.ijresmar.2019.07.002
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), 468-488. https://doi.org/10.1177/0022243720907578 DOI: https://doi.org/10.1177/0022243720907578
La Rotta, D., Pérez Rave, J. (2017). A relevant literary space on the use of the European Foundation for Quality Management model: current state and challenges. Total Quality Management & Business Excellence, 28(13-14), 1447-1468. https://doi.org/10.1080/14783363.2016.1150168 DOI: https://doi.org/10.1080/14783363.2016.1150168
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. https://doi.org/10.37394/23207.2020.17.41 DOI: https://doi.org/10.37394/23207.2020.17.41
Lee, M., Cai, Y., DeFranco, A., & Lee, J. (2020). Exploring influential factors affecting guest satisfaction: Big data and business analytics in consumer-generated reviews. Journal of Hospitality and Tourism Technology, 11(1), 137-153. https://www.emerald.com/insight/content/doi/10.1108/JHTT-07-2018-0054/full/html DOI: https://doi.org/10.1108/JHTT-07-2018-0054
Li, L., Li, X., Qi, W., Zhang, Y., & Yang, W. (2022). Targeted reminders of electronic coupons: using predictive analytics to facilitate coupon marketing. Electronic Commerce Research, 22, 321-350. https://doi.org/10.1007/s10660-020-09405-4 DOI: https://doi.org/10.1007/s10660-020-09405-4
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://www.emerald.com/insight/content/doi/10.1108/APJML-03-2019-0211/full/html DOI: https://doi.org/10.1108/APJML-03-2019-0211
Liu, X., Shin, H., & Burns, A. C. (2021). Examining the impact of luxury brand’s social media marketing on customer engagement: Using big data analytics and natural language processing. Journal of Business Research, 125, 815-826. https://doi.org/10.1016/j.jbusres.2019.04.042 DOI: https://doi.org/10.1016/j.jbusres.2019.04.042
Louro, A. C., Brandão, M. M., Jaklič, J., & Sarcinelli, A. (2019). How can customer analytics capabilities influence organizational performance? A moderated mediation analysis. Brazilian Business Review, 16(4), 369-382. https://www.scielo.br/j/bbr/a/MtRBBCqz5DktnNyxqSMKj4g/?lang=en DOI: https://doi.org/10.15728/bbr.2019.16.4.4
Lu, J., Cairns, L., & Smith, L. (2021). Data science in the business environment: customer analytics case studies in SMEs. Journal of Modelling in Management, 16(2), 689-713. https://www.emerald.com/insight/content/doi/10.1108/JM2-11-2019-0274/full/html DOI: https://doi.org/10.1108/JM2-11-2019-0274
Macke, J., Genari, D. (2019). Systematic literature review on sustainable human resource management. Journal of Cleaner Production, 208, 806-815. https://doi.org/10.1016/j.jclepro.2018.10.091 DOI: https://doi.org/10.1016/j.jclepro.2018.10.091
Malek, J., Desai, T. N. (2020). A systematic literature review to map literature focus of sustainable manufacturing. Journal of Cleaner Production, 256, 120345. https://doi.org/10.1016/j.jclepro.2020.120345 DOI: https://doi.org/10.1016/j.jclepro.2020.120345
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
Michaelidou, N., Micevski, M. (2019). Consumers’ ethical perceptions of social media analytics practices: Risks, benefits and potential outcomes. Journal of Business Research, 104, 576-586. https://doi.org/10.1016/j.jbusres.2018.12.008 DOI: https://doi.org/10.1016/j.jbusres.2018.12.008
Mio, C., Panfilo, S., & Blundo, B. (2020). Sustainable development goals and the strategic role of business: A systematic literature review. Business Strategy and the Environment, 29(8), 3220-3245. https://doi.org/10.1002/bse.2568 DOI: https://doi.org/10.1002/bse.2568
Nethravathi, R., Sathyanarayana, P., Vidya Bai, G., Spulbar, C., Suhan, M., Birau, R., & Ejaz, A. (2020). Business intelligence appraisal based on customer behaviour profile by using hobby based opinion mining in India: a case study. Economic Research-Ekonomska Istraživanja, 33(1), 1889-1908. https://doi.org/10.1080/1331677X.2020.1763822 DOI: https://doi.org/10.1080/1331677X.2020.1763822
Pantano, E., Giglio, S., & Dennis, C. (2019). Making sense of consumers’ tweets: Sentiment outcomes for fast fashion retailers through Big Data analytics. International Journal of Retail & Distribution Management, 47(9), 915-927. https://doi.org/10.1108/IJRDM-07-2018-0127 DOI: https://doi.org/10.1108/IJRDM-07-2018-0127
Parmar, P. S., Desai, T. N. (2019). A systematic literature review on Sustainable Lean Six Sigma: Current status and future research directions. International Journal of Lean Six Sigma, 11(3), 429-461. https://doi.org/10.1108/IJLSS-08-2018-0092 DOI: https://doi.org/10.1108/IJLSS-08-2018-0092
Pérez-Rave, J. I. (2012). Revisión Sistemática de Literatura de Ingeniería. Universidad de Antioquia. https://www.idinnov.com/product/revision-sistematica-de-literatura-en-ingenieria-2a-ed/
Pérez-Rave, J. I. (2019). Revisión Sistemática de Literatura de Ingeniería (2ª ed.). IDINNOV. https://www.idinnov.com/product/revision-sistematica-de-literatura-en-ingenieria-2a-ed/
Pérez-Rave, J. I., Jaramillo-Álvarez, G. P., & Correa-Morales, J. C. (2022). Multi-criteria decision-making leveraged by text analytics and interviews with strategists. Journal of Marketing Analytics, 10(1), 30. https://doi.org/10.1057%2Fs41270-021-00125-8 DOI: https://doi.org/10.1057/s41270-021-00125-8
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), 675-686. https://www.emerald.com/insight/content/doi/10.1108/JCM-02-2019-3080/full/html DOI: https://doi.org/10.1108/JCM-02-2019-3080
Prašnikar, J., Lisjak, M., Buhovac, A. R., & Štembergar, M. (2008). Identifying and exploiting the inter relationships between technological and marketing capabilities. Long Range Planning, 41(5), 530-554. https://doi.org/10.1016/j.lrp.2008.06.005 DOI: https://doi.org/10.1016/j.lrp.2008.06.005
Raeesi-Vanani, I. (2019). Text analytics of customers on twitter: Brand sentiments in customer support. Journal of Information Technology Management, 11(2), 43-58. https://jitm.ut.ac.ir/e_73947_161dfbbd02dc246360bf20660ae7c959.pdf
Rajan, P. (2019). The effectiveness of social media content marketing towards brand health of a company: Social media analytics. International Journal of Scientific & Technology Research, 8(11), 1188-1192. https://api.semanticscholar.org/CorpusID:219885626
Rajendran, S. (2021). 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), 473-493. 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. M. (2019). Big data analytics implementation in banking industry–Case study cross selling activity in Indonesia’s Commercial bank. International Journal of Scientific & Technology Research, 8(9), 1632-1643. https://api.semanticscholar.org/CorpusID:204901775
Ramana, A. V., Rao, A. S. & Reddy, E. K. (2019). Applications of business intelligence and decision-making for customer behavior analysis in telecom industry. International Journal of Recent Technology and Engineering, 7(6S2). https://www.ijrte.org/wp-content/uploads/papers/v7i6s2/F11150476S219.pdf
Rosado-Pinto, F., Loureiro, S. M. C. (2020). The growing complexity of customer engagement: A systematic review. EuroMed Journal of Business, 15(2), 167-203. https://doi.org/10.1108/EMJB-10-2019-0126 DOI: https://doi.org/10.1108/EMJB-10-2019-0126
Rose, S., Sreejith, R., & Senthil, S. (2019). Social media data analytics to improve the customer services: the case of fast-food companies. International Journal of Recent Technology and Engineering, 8(2), 6359-6366. http://www.doi.org/10.35940/ijrte.B2205.078219 DOI: https://doi.org/10.35940/ijrte.B2205.078219
Schotten, M., Meester, W. J., Steiginga, S., & Ross, C. A. (2017). A brief history of Scopus: The world’s largest abstract and citation database of scientific literature. In Research Analytics: Boosting University Productivity and Competitiveness through Scientometrics (pp. 31-58). CRC Press. https://doi.org/10.1201/9781315155890 DOI: https://doi.org/10.1201/9781315155890-3
Seidlova, R., Poživil, J., & Seidl, J. (2019). Marketing and business intelligence with help of ant colony algorithm. Journal of Strategic Marketing, 27(5), 451-463. https://www.tandfonline.com/doi/full/10.1080/0965254X.2018.1430058 DOI: https://doi.org/10.1080/0965254X.2018.1430058
Sohrabi, B., Raeesi Vanani, I., 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(3), 346-363. https://doi.org/10.1108/IJPHM-11-2017-0066 DOI: https://doi.org/10.1108/IJPHM-11-2017-0066
Tasci, A. D. (2020). Exploring the analytics for linking consumer-based brand equity (CBBE) and financial-based brand equity (FBBE) of destination or place brands. Place Branding and Public Diplomacy, 16, 36-59. https://link.springer.com/article/10.1057/s41254-019-00125-7 DOI: https://doi.org/10.1057/s41254-019-00125-7
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533. https://josephmahoney.web.illinois.edu/BA545_Fall%202022/Teece,%20Pisano%20and DOI: https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
Velásquez Velásquez, M., Mora Cardona, E. C., & Pérez Rave, J. I. (2022). Modelo conceptual e instrumento sobre las funciones de la oficina de gestión de proyectos en ámbitos educativos. Ingeniare. Revista Chilena de Ingeniería, 30(2), 321-342. https://www.scielo.cl/scielo.php?pid=S0718-33052022000200321&script=sci_arttext&tlng=en DOI: https://doi.org/10.4067/S0718-33052022000200321
Vieira, E. S., Gomes, J. A. (2009). A comparison of Scopus and Web of Science for a typical university. Scientometrics, 81, 587-600. https://link.springer.com/article/10.1007/s11192-009-2178-0 DOI: https://doi.org/10.1007/s11192-009-2178-0
Vorhies, D. W. (1998). An investigation of the factors leading to the development of marketing capabilities and organizational effectiveness. Journal of Strategic Marketing, 6(1), 3-23. https://doi.org/10.1080/096525498346676 DOI: https://doi.org/10.1080/096525498346676
Vorhies, D. W., Orr, L. M., & Bush, V. D. (2011). Improving customer-focused marketing capabilities and firm financial performance via marketing exploration and exploitation. Journal of the Academy of Marketing Science, 39, 736-756. https://link.springer.com/article/10.1007/s11747-010-0228-z DOI: https://doi.org/10.1007/s11747-010-0228-z
Wagman, P., Håkansson, C. (2019). Occupational balance from the interpersonal perspective: A scoping review. Journal of Occupational Science, 26(4), 537-545. https://doi.org/10.1080/14427591.2018.1512007 DOI: https://doi.org/10.1080/14427591.2018.1512007
Wang, Y., Zhang, M., Tse, Y. K., & Chan, H. K. (2020). Unpacking the impact of social media analytics on customer satisfaction: do external stakeholder characteristics matter? International Journal of Operations & Production Management, 40(5), 647-669. https://doi.org/10.1108/IJOPM-04-2019-0331 DOI: https://doi.org/10.1108/IJOPM-04-2019-0331
Yang, Y., See-To, E. W., & Papagiannidis, S. (2020). You have not been archiving emails for no reason! Using big data analytics to cluster B2B interest in products and services and link clusters to financial performance. Industrial Marketing Management, 86, 16-29. https://doi.org/10.1016/j.indmarman.2019.01.016 DOI: https://doi.org/10.1016/j.indmarman.2019.01.016
Yerpude, S., Singhal, T. K. (2021). “Custolytics” Internet of Things based customer analytics aiding customer engagement strategy in emerging markets–an empirical research. International Journal of Emerging Markets, 16(1), 92-112. https://doi.org/10.1108/IJOEM-05-2018-0250 DOI: https://doi.org/10.1108/IJOEM-05-2018-0250
Zhang, H., & Xiao, Y. (2020). Customer involvement in big data analytics and its impact on B2B innovation. Industrial Marketing Management, 86, 99-108. https://doi.org/10.1016/j.indmarman.2019.02.020 DOI: https://doi.org/10.1016/j.indmarman.2019.02.020
Similar Articles
- María Alejandra Maya Restrepo, Jorge Iván Pérez Rave, Design and psychometric validation of a Customer Analytics Capabilities (CAC) scale: empirical evidence in Colombian organizations , Cuadernos de Administración: Vol. 40 No. 78 (2024)
- Araceli Manco Zapata, Ivan Roberto Cortes Gómez, Diagnosing the Impact of Digital Transformation on the Human Talent of SMEs in Bogotá, Colombia , Cuadernos de Administración: Vol. 39 No. 75 (2023)
- John Willmer Escobar, Issue Overview , Cuadernos de Administración: Vol. 40 No. 79 (2024)
- Maria G. Morales-González, Jannett Ayup-González, Pilar A. Huerta-Zavala, Sustainability marketing strategies of companies in Mexico , Cuadernos de Administración: Vol. 38 No. 72 (2022)
You may also start an advanced similarity search for this article.
Accepted 2024-07-18
Published 2024-08-12
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.