Palabras clave
Image
Scale Development
Retail Marketing
Cómo citar
Resumen
Purpose – This study develops and validates a measurement scale for assessing the corporate image of companies that use data-driven marketing in their decision-making and actions in online retailing.
Theoretical framework – The study is grounded in theories of corporate image and consumer behaviour, integrating concepts of data-driven marketing and privacy to develop the DDMI scale.
Design/methodology/approach – A mixed methods approach is employed, beginning with a deductive literature review and qualitative expert interviews to generate scale items, followed by a pilot study and a large-scale survey of 301 consumers via Amazon MTurk. Exploratory and confirmatory factor analyses are conducted to validate the scale.
Findings – DDM strategies significantly affect how customers perceive a company’s image. The DDMI provides a validated scale that measures aspects that are important to customers, such as privacy concerns and personalised customer experience. It reveals that effective communication, efficient payment processes and robust customer support are vital for a positive corporate image.
Practical & social implications – The study offers a novel tool to assess corporate image in environments characterised by high data usage. It enables companies to refine their DDM strategies by identifying how specific practices affect consumer perceptions.
Originality/value – This research introduces the first validated scale to measure consumer perceptions of corporate image in DDM contexts. It advances marketing theory by capturing key dimensions of the digital era, personalisation, privacy and support.
Citas
Acemoglu, D., Makhdoumi, A., Malekian, A., & Ozdaglar, A., (2022). Too Much Data: Prices and Inefficiencies in Data Markets. American Economic Journal: Microeconomics, 14(4), 218–256. https://doi.org/10.1257/mic.20200200
Aiolfi, S., Bellini, S., and Pellegrini, D., (2021). Data-driven digital advertising: benefits and risks of online behavioral advertising. International Journal of Retail and Distribution Management, 49(7), 1089–1110. https://doi.org/10.1108/IJRDM-10-2020-0410
Aljukhadar, M., Belisle, J.-F., Dantas, D. C., Sénécal, S., & Titah, R., (2022). Measuring the service quality of governmental sites: Development and validation of the e-Government service quality (EGSQUAL) scale. Electronic Commerce Research and Applications, 55, 101182. https://doi.org/10.1016/j.elerap.2022.101182
Andreassen, T. W., & Lindestad, B. (1998). The effect of corporate image in the formation of customer loyalty. Journal of Service Research, 1(1), 82–92. https://doi.org/10.1177/109467059800100107
Bandara, R., Fernando, M., & Akter, S., (2017). The Privacy Paradox in the Data-Driven Marketplace: The Role of Knowledge Deficiency and Psychological Distance. Procedia Computer Science, 121, 562–567. https://doi.org/10.1016/j.procs.2017.11.074
Barich, H., & Kotler, P., (1991). A Framework for Marketing Image Management. MIT Sloan Management Review, 32(2), 94–104.
Bashar, A., Atif, M., Rabbani, M. R., Zulfikar, Z., Selim, M., & Naseem, Y., (2022). Excelling Customer Experience Through Data Driven Behavioral Intelligence: A conceptual framework. 2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022, 634–638. https://doi.org/10.1109/ICDABI56818.2022.10041675
Bernstein, D., (1984). La imagen de la empresa y la realidad. Crítica de las comunicaciones corporativas. Plaza & Janes, Madrid.
Bhavnani, S. P., Narula, J., & Sengupta, P. P., (2016). Mobile technology and the digitisation of healthcare. European Heart Journal, 37(18), 1428–1438. https://doi.org/10.1093/eurheartj/ehv770
Boerman, S. C., Kruikemeier, S., & Zuiderveen Borgesius, F. J., (2017). Online Behavioral Advertising: A Literature Review and Research Agenda. Journal of Advertising, 46(3), 363–376. https://doi.org/10.1080/00913367.2017.1339368
Borges, M., Bernardino, J., & Pedrosa, I., (2021). Data-driven decision making strategies applied to marketing. Iberian Conference on Information Systems and Technologies, CISTI. https://doi.org/10.23919/CISTI52073.2021.9476506
Braverman, S., (2015). Global review of data-driven marketing and advertising. Journal of Direct, Data and Digital Marketing Practice, 16(3), 181–183. https://doi.org/10.1057/dddmp.2015.7
Burt, S., Johansson, U., & Thelander, Å. (2007). Retail image as seen through consumers' eyes: Studying international retail image through consumer photographs of stores, International Review of Retail, Distribution and Consumer Research, 17(5), 447-467. https://doi.org/10.1080/09593960701631516
Camilleri, M. A. (2020). The use of data-driven technologies for customer-centric marketing. International Journal of Big Data Management, 1(1), 50-63. https://doi.org/10.1504/IJBDM.2020.106876
Cheng, E. W. L., (2001). SEM being more effective than multiple regression in parsimonious model testing for management development research. Journal of Management Development, 20(7), 262–1711. https://doi.org/10.1108/02621710110400564
Chinchanachokchai, S., Thontirawong, P., & Chinchanachokchai, P., (2021). A tale of two recommender systems: The moderating role of consumer expertise on artificial intelligence based product recommendations. Journal of Retailing and Consumer Services, 61, 102528.
Costa, S. B. D., Claro, D. P., & Bortoluzzo, A. B. (2018). The influence of customer value on word of mouth: a study of the financial services company in Brazil. Revista Brasileira de Gestão de Negócios, 20, 210-231. https://doi.org/10.1016/j.jretconser.2021.102528
Clark, L. A., & Watson, D., (1995). Constructing Validity: Basic Issues in Objective Scale Development. Psychological Assessment, 7(3), 309–312. https://doi.org/10.1037/1040-3590.7.3.309
De Luca, L. M., Herhausen, D., Troilo, G., & Rossi, A., (2021). How and when do big data investments pay off? The role of marketing affordances and service innovation. Journal of the Academy of Marketing Science, 49(4), 790–810. https://doi.org/10.1007/s11747-020-00739-x
Dekimpe, M. G., (2020). Retailing and retailing research in the age of big data analytics. International Journal of Research in Marketing, 37(1), 3–14. https://doi.org/10.1016/j.ijresmar.2019.09.001
Dennis, S. A., Goodson, B. M., & Pearson, C. A., (2020). Online Worker Fraud and Evolving Threats to the Integrity of MTurk Data: A Discussion of Virtual Private Servers and the Limitations of IP-Based Screening Procedures. Behavioral Research in Accounting, 32(1), 119–134. https://doi.org/10.2308/bria-18-044
DeVellis, R. F., & Thorpe, C. T., (2021). Scale development: Theory and applications. Sage publications.
Dichter, E. (1985). What's In An Image, Journal of Consumer Marketing, 2(1), 75-81. https://doi.org/10.1108/eb038824
Dolnicar, S., Kaiser, S., Lazarevski, K., & Leisch, F., (2012). Biclustering: Overcoming data dimensionality problems in market segmentation. Journal of Travel Research, 51(1), 41–49. https://doi.org/10.1177/0047287510394192
Dremel, C., Herterich, M. M., Wulf, J., & Vom Brocke, J., (2020). Actualising big data analytics affordances: A revelatory case study. Information and Management, 57(1), 103121. https://doi.org/10.1016/j.im.2018.10.007. https://doi.org/10.1016/j.im.2018.10.007
Ettl, M., Harsha, P., Papush, A., & Perakis, G., (2020). A data-driven approach to personalised bundle pricing and recommendation. Manufacturing and Service Operations Management, 22(3), 461–480. https://doi.org/10.1287/msom.2018.0756
Flavián, C., Torres, E., & Guinalíu, M., (2004). Corporate image measurement: A further problem for the tangibilization of Internet banking services. International Journal of Bank Marketing, 22(5), 366–384. https://doi.org/10.1108/02652320410549665
Gaskin, J. (2021). Validity Master. Gaskination's StatWiki. http://statwiki.gaskination.com.
Gawankar, S. A., Gunasekaran, A., & Kamble, S., (2020). A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context. International Journal of Production Research, 58(5), 1574–1593. https://doi.org/10.1080/00207543.2019.1668070
Gentile, C., Spiller, N., & Noci, G., (2007). How to Sustain the Customer Experience: An Overview of Experience Components that Co-create Value With the Customer. European Management Journal, 25(5), 395–410. https://doi.org/10.1016/j.emj.2007.08.005
Glass, R., & Callahan, S., (2015). The Big Data-Driven Business: How to Use Big Data to Win Customers, Beat Competitors, and Boost Profits. In Research- Technology Management. John Wiley & Sons, Inc.
Graciola, A. P., De Toni, D., Milan, G. S., & Eberle, L., (2020). Mediated-moderated effects: High and low store image, brand awareness, perceived value from mini and supermarkets retail stores. Journal of Retailing and Consumer Services, 55. https://doi.org/10.1016/j.jretconser.2020.102117
Grandhi, B., Patwa, N., & Saleem, K., (2020). Data-driven marketing for growth and profitability. EuroMed Journal of Business, 16(4), 381–398. https://doi.org/10.1108/EMJB-09-2018-0054
Grover, V., Chiang, R. H. L., Liang, T.-P., & Zhang, D., (2018). Creating Strategic Business Value from Big Data Analytics: A Research Framework. Journal of Management Information Systems, 35(2), 388–423. https://doi.org/10.1080/07421222.2018.1451951
Hao, C., & Yang, L., (2022). Platform advertising and targeted promotion: Paid or free? Electronic Commerce Research and Applications, 55. https://doi.org/10.1016/j.elerap.2022.101178
Holmlund, M., Van Vaerenbergh, Y., Ciuchita, R., Ravald, A., Sarantopoulos, P., Ordenes, F. V., & Zaki, M., (2020). Customer experience management in the age of big data analytics: A strategic framework. Journal of Business Research, 116, 356–365. https://doi.org/10.1016/j.jbusres.2020.01.022
Hopkins, C. D., & Alford, B. L., (2006). Pioneering the development of a scale to measure e-tailer image. Journal of Internet Commerce, 4(4), 77–99. https://doi.org/10.1300/J179v04n04_06
Hoyer, W. D., Kroschke, M., Schmitt, B., Kraume, K., & Shankar, V., (2020). Transforming the Customer Experience Through New Technologies. Journal of Interactive Marketing, 51, 57–71. https://doi.org/10.1016/j.intmar.2020.04.001
Ivanov, A., Mou, Y., & Tawira, L., (2022). Avatar personalisation vs. Privacy in a virtual try-on app for apparel shopping. International Journal of Fashion Design, Technology and Education, 16(1), 100-109. https://doi.org/10.1080/17543266.2022.2125589
Jabbar, A., Akhtar, P., & Dani, S., (2020). Real-time big data processing for instantaneous marketing decisions: A problematisation approach. Industrial Marketing Management, 90, 558 – 569. https://doi.org/10.1016/j.indmarman.2019.09.001
Jackson, D. A., (1993). Stopping rules in principal components analysis: a comparison of heuristical and statistical approaches. Ecological Society of America, 74(8), 2204–2214. https://doi.org/10.2307/1939574
Johnson, D., Sihi, D., Muzelle, L., & Zahay, D., (2019). The Marketing Organization's Journey to Become Data-Driven. Journal of Research in Interactive Marketing, 13(2), 162–178. https://doi.org/10.1108/JRIM-12-2018-0157
Jurkiewicz, C. L., (2018). Big Data, Big Concerns: Ethics in the Digital Age. Public Integrity, 20(1), 46–S59. https://doi.org/10.1080/10999922.2018.1448218
Kock, N., (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of E-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
Kushwaha, A. K., Kumar, P., & Kar, A. K., (2021). What impacts customer experience for B2B enterprises on using AI-enabled chatbots? Insights from Big data analytics. Industrial Marketing Management, 98, 207 – 221. https://doi.org/10.1016/j.indmarman.2021.08.011
Liu, H., Huang, Y., Wang, Z., Liu, K., Hu, X., & Wang, W., (2019). Personality or value: A comparative study of psychographic segmentation based on an online review enhanced recommender system, Applied Sciences, 9(10), 1992. https://doi.org/10.3390/app9101992. https://doi.org/10.3390/app9101992
Ma, D., & Hu, J., (2020). Research on collaborative management strategies of closed-loop supply chain under the influence of big-data marketing and reference price effect. Sustainability, 12(4), 1685. https://doi.org/10.3390/su12041685
Madadi, R., Torres, I. M., Fazli-Salehi, R., & Zúñiga, M. Á., (2021). The Impact of Hispanic-Targeted Advertising on Consumers' Brand Love in Services. Journal of International Consumer Marketing, 33(2), 137 – 158. https://doi.org/10.1080/08961530.2020.1767527
Mao, W., Zheng, Z., & Wu, F., (2019). Pricing for Revenue Maximization in IoT Data Markets: An Information Design Perspective. Proceedings - IEEE INFOCOM, 2019-April, 1837–1845. https://doi.org/10.1016/j.jbusres.2022.03.026
Mero, J., Leinonen, M., Makkonen, H., & Karjaluoto, H., (2022). Agile logic for SaaS implementation: Capitalising on marketing automation software in a start-up. Journal of Business Research, 145, 583 – 594. https://doi.org/10.1016/j.jbusres.2022.03.026
Mero, J., Tarkiainen, A., & Tobon, J., (2020). Effectual and causal reasoning in the adoption of marketing automation. Industrial Marketing Management, 86, 212 – 222. https://doi.org/10.1016/j.indmarman.2019.12.008
Meyer, C., & Schwager, A., (2007). Understanding customer experience. Harvard Business Review, 85(2), 116–26, 157.
Miklosik, A., & Evans, N., (2020). Impact of Big Data and Machine Learning on Digital Transformation in Marketing: A Literature Review. IEEE Access, 8, 101284–101292. https://doi.org/10.1109/ACCESS.2020.2998754
Mishra, S., Malhotra, G., &Tiwari, S. R. (2021). Moderating effect of cognitive conflict on the relationship between value consciousness and online shopping cart abandonment. The International Review of Retail, Distribution and Consumer Research, 31(5), 511-530. https://doi.org/10.1080/09593969.2021.2002386
Félix, B. M., Tavares, E., & Cavalcante, N. W. F. (2018). Critical success factors for Big Data adoption in the virtual retail: Magazine Luiza case study. Revista Brasileira de Gestão de Negócios, 20, 112-126. https://doi.org/10.7819/rbgn.v20i1.3627
Abou-foul, M. Ruiz-Alba, J. L., & Soares, A., (2021). The impact of digitalisation and servitisation on the financial performance of a firm: an empirical analysis. Production Planning & Control, 32(12), 975–989. https://doi.org/10.1080/09537287.2020.1780508
Morgan, T., Anokhin, S. A., & Wincent, J., (2019). Influence of market orientation on performance: the moderating roles of customer participation breadth and depth in new product development. Industry and Innovation, 26(9), 1103–1120. https://doi.org/10.1080/13662716.2019.1566053
Mulvenna, M., Norwood, M., & Büchner, A., (1998). Data-Driven Marketing. Electronic Markets, 8(3), 32–35. https://doi.org/10.1080/10196789800000038
Nassar, A., & Kamal, M., (2021). Ethical Dilemmas in AI-Powered Decision-Making: A Deep Dive into Big Data-Driven Ethical Considerations. International Journal of Responsible Artificial Intelligence, 11(8), 1–11.
Ngai, E. W. T., Lee, M. C. M., Luo, M., Chan, P. S. L., & Liang, T., 2021. An intelligent knowledge-based chatbot for customer service. Electronic Commerce Research and Applications, 50, 101098. https://doi.org/10.1016/j.elerap.2021.101098
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E., (2019). Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries. Frontiers in Big Data, 2. https://doi.org/10.3389/fdata.2019.00013
Özkan, P., Süer, S., Keser, İ. K., & Kocakoç, İ. D., (2020). The effect of service quality and customer satisfaction on customer loyalty: The mediation of perceived value of services, corporate image, and corporate reputation. International Journal of Bank Marketing, 38(2), 384 – 405. https://doi.org/10.1108/IJBM-03-2019-0096
Pantano, E., (2019). The role of smart technologies in decision making: Developing, supporting and training smart consumers. Journal of Marketing Management, 35(15–16), 1367–1369. https://doi.org/10.1080/0267257X.2019.1688927
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P., (2003). Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. In Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Rahman, M. S., Hossain, M. A., Abdel Fattah, F. A. M., & Akter, S., (2020). Optimising competitive performance of service firms in data-rich environment. Journal of Service Theory and Practice, 30(6), 681–706. https://doi.org/10.1108/JSTP-04-2020-0077
Rose, S., Clark, M., Samouel, P., & Hair, N., (2012). Online Customer Experience in e-Retailing: An empirical model of Antecedents and Outcomes. Journal of Retailing, 88(2), 308–322. https://doi.org/10.1016/j.jretai.2012.03.001
Saura, J. R., Palacios-Marqués, D., & Ribeiro-Soriano, D., (2021). Digital marketing in SMEs via data-driven strategies: Reviewing the current state of research. Journal of Small Business Management, 61(3), 1278–1313. https://doi.org/10.1080/00472778.2021.1955127
Saura, J. R., Reyes-Menendez, A., & Filipe, F., (2019). Comparing data-driven methods for extracting knowledge from user generated content. Journal of Open Innovation: Technology, Market, and Complexity, 5(4), 74.
Shah, D., & Murthi, B. P. S., (2021). Marketing in a data-driven digital world: Implications for the role and scope of marketing. Journal of Business Research, 125, 772–779. https://doi.org/10.1016/j.jbusres.2020.06.062
Sheth, J., (2021). New areas of research in marketing strategy, consumer behavior, and marketing analytics: the future is bright. Journal of Marketing Theory and Practice, 29(1), 3–12. https://doi.org/10.1080/10696679.2020.1860679
Shin, J., & Yu, J., (2021). Targeted advertising and consumer inference. Marketing Science, 40(5), 900 – 922. https://doi.org/10.1287/mksc.2021.1284
Silva, S. C., Corbo, L., Vlačić, B., & Fernandes, M., (2023). Marketing accountability and marketing automation: evidence from Portugal. EuroMed Journal of Business, 18(1), 145 – 164. https://doi.org/10.1108/EMJB-11-2020-0117
Sirgy, M. J., Grewal, D., Mangleburg, T. F., Park, J. O., Chon, K. S., Claiborne, C. B., Johar, J. S., & Berkman, H., (1997). Assessing the Predictive Validity of Two Methods of Measuring Self-Image Congruence. Journal of the Academy of Marketing Science, 25(3), 229–241. https://doi.org/10.1177/0092070397253004
Spector, A. J., (1961). Basic Dimensions of the Corporate Image. Journal of Marketing, 25(6), 47–51. https://doi.org/10.1177/002224296102500608
Stratton, S. J., (2021). Population Research: Convenience Sampling Strategies. Prehospital and Disaster Medicine, 36(4), 373–374. https://doi.org/10.1017/S1049023X21000649
Sultana, S., Akter, S., & Kyriazis, E., (2022). Theorising Data-Driven Innovation Capabilities to Survive and Thrive in the Digital Economy. Journal of Strategic Marketing. https://doi.org/10.1080/0965254X.2021.2013934
Tong, S., Luo, X., & Xu, B., (2020). Personalised mobile marketing strategies. Journal of the Academy of Marketing Science, 48(1), 64–78. https://doi.org/10.1007/s11747-019-00693-3
Trabucchi, D., & Buganza, T., (2019). Data-driven innovation: switching the perspective on Big Data. European Journal of Innovation Management, 22(1), 23–40. https://doi.org/10.1108/EJIM-01-2018-0017
Tran, M. A., Nguyen, B., Melewar, T. C., & Bodoh, J., (2015). Exploring the corporate image formation process. Qualitative Market Research, 18(1), 86–114. https://doi.org/10.1108/QMR-05-2014-0046
Tripathi, A., Bagga, T., Sharma, S., & Vishnoi, S. K., (2021). Big Data-Driven Marketing enabled Business Performance: A conceptual framework of information, strategy and customer lifetime value. Proceedings of the Confluence 2021: 11th International Conference on Cloud Computing, Data Science and Engineering, 315–320. https://doi.org/10.1109/Confluence51648.2021.9377156
Tyrväinen, O., Karjaluoto, H., & Saarijärvi, H., (2020). Personalisation and hedonic motivation in creating customer experiences and loyalty in omnichannel retail. Journal of Retailing and Consumer Services, 57. https://doi.org/10.1016/j.jretconser.2020.102233
Valdez-Mendia, J. M., & Flores-Cuautle, J. J. A., (2022). Toward customer hyper-personalisation experience — A data-driven approach. Cogent Business and Management, 9(1). https://doi.org/10.1080/23311975.2022.2041384
Varnali, K., (2021). Online behavioral advertising: An integrative review. Journal of Marketing Communications, 27(1), 93–114. https://doi.org/10.1080/13527266.2019.1630664
Verhoef, P. C., (2021). Omni-channel retailing: some reflections. Journal of Strategic Marketing, 29(7), 608–616. https://doi.org/10.1080/0965254X.2021.1892163
Vijaya, A. P., & Rahayu, M., (2021). The effect of Product Innovation and Service Quality on Competitive Advantage mediated by Company Image (Study at PT. Toyota Astra Motor in Malang Raya). International Journal of Business, Economics and Law, 24(4), 148–154.
Wang, P., Li, K., Du, Q., & Wang, J., (2024). Customer experience in AI-enabled products: Scale development and validation. Journal of Retailing and Consumer Services, 76, 103578. https://doi.org/10.1016/j.jretconser.2023.103578
Waldfogel, J., (2017). How Digitization Has Created a Golden Age of Music, Movies, Books, and Television. Journal of Economic Perspectives, 31(3), 195–214. https://doi.org/10.1257/jep.31.3.195
Wedel, M., & Kannan, P. K., (2016). Marketing Analytics for Data-Rich Environments, Journal of marketing, 80(6), 97-121. https://doi.org/10.1509/jm.15.0413
Wessling, K. S., Huber, J., & Netzer, O., (2017). MTurk character misrepresentation: Assessment and solutions. Journal of Consumer Research, 44(1), 211–230. https://doi.org/10.1093/jcr/ucx053
Williams, B., Onsman, A., & Brown, T., (2010). Exploratory factor analysis: A five-step guide for novices. Australasian Journal of Paramedicine, 8, 1–13. https://doi.org/10.33151/ajp.8.3.93
Woerner, S. L., & Wixom, B. H., (2015). Big Data: Extending the Business Strategy Toolbox. Journal of Information Technology, 30(1), 60–62. https://doi.org/10.1057/jit.2014.31
Xiang, Z., (2018). From digitisation to the age of acceleration: On information technology and tourism. Tourism Management Perspectives, 25, 147–150. https://doi.org/10.1016/j.tmp.2017.11.023
Xiao, J., Wang, M., Jiang, B., & Li, J., (2018). A personalised recommendation system with combinational algorithm for online learning. Journal of Ambient Intelligence and Humanized Computing, 9(3), 667–677. https://doi.org/10.1007/s12652-017-0466-8
Zhang, C., Wang, X., Cui, A. P., & Han, S., (2020). Linking big data analytical intelligence to customer relationship management performance. Industrial Marketing Management, 91, 483–494. https://doi.org/10.1016/j.indmarman.2020.10.012
Zhang, Y., (2015). The impact of brand image on consumer behaviour: A literature review. Open Journal of Business and Management, 3(01), 58. https://doi.org/10.4236/ojbm.2015.31006
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