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The impact of AI-supported marketing capabilities and analytics on SMEs' customer agility and marketing performance

Yıl 2025, Cilt: 11 Sayı: 1, 1 - 14
https://doi.org/10.24289/ijsser.1601570

Öz

This research examines the impact of marketing analytics and artificial intelligence applications on customer agility and marketing performance in businesses that adopt e-commerce. In this quantitative study, data were collected through a questionnaire. Data collected from 227 managers online were analyzed using the Smart PLS method. The study concluded that marketing analytics and AI-supported marketing capabilities affect customer agility and marketing performance. It is also concluded that customer agility has an impact on marketing performance. In addition, the results show that customer agility is a mediator of the effects of AI-supported marketing capabilities and analytics on marketing performance. It offers concrete suggestions for businesses, facilitating decision-making processes, and demonstrates how digital marketing strategies can be employed more effectively. The study also makes an academic contribution by analyzing the relationship between digital transformation and marketing capabilities, thus guiding future research.

Kaynakça

  • Agag, G., Shehawy, Y. M., Almoraish, A., Eid, R., Lababdi, H. C., Labben, T. G., & Abdo, S. S. (2024). Understanding the relationship between marketing analytics, customer agility, and customer satisfaction: A longitudinal perspective. Journal of Retailing and Consumer Services, 77, 1-13.
  • Akter, S., Hani, U., Dwivedi, Y. K., & Sharma, A. (2022). The future of marketing analytics in the sharing economy. Industrial Marketing Management, 104, 85-100.
  • Akter, S., Bandara, R. J., & Sajib, S. (2021). How to empower analytics capability to tackle emergency situations?. International Journal of Operations & Production Management, 41(9), 1469-1494.
  • Akter, S., Bandara, R., Hani, U., Wamba, S. F., Foropon, C., & Papadopoulos, T. (2019). Analytics-based decision-making for service systems: A qualitative study and agenda for future research. Int. Journal of Information Management, 48, 85-95.
  • Akter, S., Hossain, M. A., Tarba, S. Y., & Leonidou, E. (2023). How does quality-dominant logic ensure marketing analytics success and tackle business failure in industrial markets?. Industrial Marketing Management, 109, 44-57.
  • Alghamdi, O., & Agag, G. (2024). Competitive advantage: A longitudinal analysis of the roles of data-driven innovation capabilities, marketing agility, and market turbulence. Journal of Retailing and Consumer Services, 76, 1-17.
  • Ali, S., Tian, H., Wu, W., Ali, S., Kumail, T., & Saif, N. (2024). Marketing capabilities, market ambidexterity and product innovation outcomes: A yin-yang of inside-out and outside-in. Industrial Marketing Management, 118, 27-43.
  • Arslan, A., Kamara, S., Tian, A. Y., Rodgers, P., & Kontkanen, M. (2024). Marketing agility in underdog entrepreneurship: A qualitative assessment in post-conflict Sub-Saharan African context. Journal of Business Research, 173, 1-18.
  • Ashrafi, A., Ravasan, A. Z., Trkman, P., & Afshari, S. (2019). The role of business analytics capabilities in bolstering firms' agility and performance. International Journal of Information Management, 47, 1-15.
  • Baabdullah, A. M., Alalwan, A. A., Slade, E. L., Raman, R., & Khatatneh, K. F. (2021). SMEs and artificial intelligence (AI): Antecedents and consequences of AI-based B2B practices. Industrial Marketing Management, 98, 255-270.
  • Barata, S. F., Ferreira, F. A., Carayannis, E. G., & Ferreira, J. J. (2024). Determinants of E-commerce, artificial intelligence, and agile methods in small-and medium-sized enterprises. IEEE Transactions on Engineering Management. 1-12.
  • Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology, 51(6), 1173.
  • Bawack, R. E., Wamba, S. F., Carillo, K. D. A., & Akter, S. (2022). Artificial intelligence in E-Commerce: a bibliometric study and literature review. Electronic markets, 32(1), 297-338.
  • Bozkurt, S. (2022). The Effect of Perceived Social Media Agility on Customer Engagement Behavior: The Regulatory Role of Social Media Usage Intensity. Social Inventor Academic Review, 3(1), 96-122.
  • Cadden, T., Weerawardena, J., Cao, G., Duan, Y., & McIvor, R. (2023). Examining the role of big data and marketing analytics in SMEs innovation and competitive advantage: A knowledge integration perspective. Journal of Business Research, 168, 1-15.
  • Çallı, B. A., & Çallı, L. (2021). Relationships between digital maturity, organizational agility, and firm performance: an empirical investigation on SMEs. Business & Management Studies: An International Journal, 9(2), 486-502.
  • Cetindas, A. (2023). Information Sharing, Agility and Customer Performance in Supply Chains: A Mediation Model. Turkish Journal of Social Sciences Research, 8(2), 134-145.
  • Chen, D., Esperança, J. P., & Wang, S. (2022). The impact of artificial intelligence on firm performance: an application of the resource-based view to e-commerce firms. Frontiers in Psychology, 13, 1-10.
  • Cheng, C. C., & Shiu, E. C. (2023). The relative values of big data analytics versus traditional marketing analytics to firm innovation: An empirical study. Information & Management, 60(7), 1-9.
  • Clark, B. H. (1999). Marketing performance measures: History and interrelationships. Journal of marketing management, 15(8), 711-732.
  • Demir, Ş. Ş., & Demir, M. (2023). Professionals' perspectives on ChatGPT in the tourism industry: Does it inspire awe or concern?. Journal of Tourism Theory and Research, 9(2), 61-77.
  • Dinç, E. A., & Kazan, H. (2023). Adaptation of Marketing Agility Scale to Turkish (Validity and Reliability Study). International Journal of Management Economics and Business, 19(4), 763-782.
  • Drydakis, N. (2022). Artificial Intelligence and reduced SMEs' business risks. A dynamic capabilities analysis during the COVID-19 pandemic. Information Systems Frontiers, 24(4), 1223-1247.
  • Dwivedi, Y. K., Sharma, A., Rana, N. P., Giannakis, M., Goel, P., & Dutot, V. (2023). Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions. Technological Forecasting and Social Change, 192, 1-9.
  • Fonseka, K., Jaharadak, A. A., & Raman, M. (2022). Impact of E-commerce adoption on business performance of SMEs in Sri Lanka; moderating role of artificial intelligence. International Journal of Social Economics, 49(10), 1518-1531.
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
  • Giacosa, E., Culasso, F., & Crocco, E. (2022). Customer agility in the modern automotive sector: how lead management shapes agile digital companies. Technological Forecasting and Social Change, 175, 1-12.
  • Hadjielias, E., Christofi, M., Christou, P., & Drotarova, M. H. (2022). Digitalization, agility, and customer value in tourism. Technological Forecasting and Social Change, 175, 1-14.
  • Hair Jr, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107-123.
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2012). Partial least squares: the better approach to structural equation modeling?. Long range planning, 45(5-6), 312-319.
  • Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial management & data systems, 116(1), 2-20.
  • Hokmabadi, H., Rezvani, S. M., & de Matos, C. A. (2024). Business Resilience for Small and Medium Enterprises and Startups by Digital Transformation and the Role of Marketing Capabilities—A Systematic Review. Systems, 12(6), 220.‏
  • Hossain, M. A., Agnihotri, R., Rushan, M. R. I., Rahman, M. S., & Sumi, S. F. (2022). Marketing analytics capability, artificial intelligence adoption, and firms' competitive advantage: Evidence from the manufacturing industry. Industrial Marketing Management, 106, 240-255.
  • Kamran, H. (2021). The use of artificial intelligence in marketing: A research on consumer acceptance of artificial intelligence marketing tools (Master's thesis, Bursa Uludag University (Turkey)).
  • Khan, A., Talukder, M. S., Islam, Q. T., & Islam, A. N. (2022). The impact of business analytics capabilities on innovation, information quality, agility and firm performance: the moderating role of industry dynamism. VINE Journal of Information and Knowledge Management Systems.1-13.
  • Khan, H. (2020). Is marketing agility important for emerging market firms in advanced markets?. International Business Review, 29(5), 1-13.
  • Kumar, A., Pandey, A., Pujari, P., & Arora, M. (2023). Adoption of AI and e-commerce improving marketing performance of SMEs. Academy of Marketing Studies Journal, 27(5). 1-15.
  • Li, L., Lin, J., Luo, W., & Luo, X. R. (2023). Investigating the effect of artificial intelligence on customer relationship management performance in e-commerce enterprises. Journal of Electronic Commerce Research, 24(1), 68-83.
  • Li, L., Lin, J., Turel, O., Liu, P., & Luo, X. (2020). The impact of e-commerce capabilities on agricultural firms' performance gains: the mediating role of organizational agility. Industrial Management & Data Systems, 120(7), 1265-1286.
  • Liang, X., Li, G., Zhang, H., Nolan, E., & Chen, F. (2022). Firm performance and marketing analytics in the Chinese context: A contingency model. Journal of Business Research, 141, 589-599.
  • Lin, F., & Eng, T. Y. (2024). Entrepreneurial performance and marketing analytics: the role of new product innovation. Journal of Small Business and Enterprise Development. 1-16.
  • Madanchian, M. (2024). The Impact of Artificial Intelligence Marketing on E-Commerce Sales. Systems, 12(10), 429-441.
  • Manis, K. T., & Madhavaram, S. (2023). AI-Supported marketing capabilities and the hierarchy of capabilities: Conceptualization, proposition development, and research avenues. Journal of Business Research, 157, 1-15.
  • Mariani, M. M., Machado, I., & Nambisan, S. (2023). Types of innovation and artificial intelligence: A systematic quantitative literature review and research agenda. Journal of Business Research, 155, 1-14.
  • Masialeti, M., Talaei-Khoei, A., & Yang, A. T. (2024). Revealing the role of explainable AI: How does updating AI applications generate agility-driven performance?. International Journal of Information Management, 77, 1-19.
  • Mehrabi, H., Chen, Y. K., & Keramati, A. (2024). Developing customer analytics capability in firms of different ages: Examining the complementarity of outside-in and inside-out resources. Industrial Marketing Management, 119, 108-121.
  • Ozdemir, S., Wang, Y., Gupta, S., Sena, V., Zhang, S., & Zhang, M. (2024). Customer analytics and new product performance: The role of contingencies. Technological Forecasting and Social Change, 201, 1-15.
  • Peretz-Andersson, E., Tabares, S., Mikalef, P., & Parida, V. (2024). Artificial intelligence implementation in manufacturing SMEs: A resource orchestration approach. International Journal of Information Management, 77, 1-11.
  • Rizvanović, B., Zutshi, A., Grilo, A., & Nodehi, T. (2023). Linking the potentials of extended digital marketing impact and start-up growth: Developing a macro-dynamic framework of start-up growth drivers supported by digital marketing. Technological Forecasting and Social Change, 186, 1-18.
  • Rossi, S., Rossi, M., Mukkamala, R. R., Thatcher, J. B., & Dwivedi, Y. K. (2024). Augmenting research methods with foundation models and generative AI. International Journal of Information Management, 1-9.
  • Salah, O. H., & Ayyash, M. M. (2024). E-commerce adoption by SMEs and its effect on marketing performance: An extended of TOE framework with ai integration, innovation culture, and customer tech-savviness. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 1-13.
  • Schramm-Klein, H., & Morschett, D. (2006). The relationship between marketing performance, logistics performance and company performance for retail companies. International Review of Retail, Distribution and Consumer Research, 16(02), 277-296.
  • Shukla, A., Varshney, J., & Raj, A. (2024). Examining the linkage between managerial ties and firm performance: The mediating role of marketing capabilities and moderation role of industry-A meta-analytic approach. Industrial Marketing Management, 119, 122-134.
  • Tarn, D. D., & Wang, J. (2023). Can data analytics raise marketing agility?-A sense-and-respond perspective. Information & Management, 60(2), 1-13.
  • Tseng, H. T., Aghaali, N., & Hajli, N. (2022). Customer agility and big data analytics in new product context. Technological Forecasting and Social Change, 180, 1-10.
  • Uğurlu, Ö. Y., Çolakoğlu, E., & Öztosun, E. (2019). The effect of strategic agility on firm performance: A research in manufacturing enterprises. Journal of Business and Human, 6(1), 93-106.
  • Wahab, M. D. A., & Radmehr, M. (2024). The impact of AI assimilation on firm performance in small and medium-sized enterprises: A moderated multi-mediation model. Heliyon, 10(8). 1-14.
  • Wamba, S. F. (2022). Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility. International Journal of Information Management, 67, 1-14.
  • Weng, Q., Wang, D., De Lurgio II, S., & Schuetz, S. (2024). How do small-to-medium-sized e-commerce businesses stay competitive? Evidence on the critical roles of IT capability, innovation and multihoming. Internet Research. 1-15.
  • Wu, Q., Yan, D., & Umair, M. (2023). Assessing the role of competitive intelligence and practices of dynamic capabilities in business accommodation of SMEs. Economic Analysis and Policy, 77, 1103-1114.
  • Yaman, T. T., & Bilgiç, E. (2021). The Role of Business Analytics in Strategic Competitiveness of Businesses. Researchgate.
  • Zahoor, N., & Lew, Y. K. (2023). Enhancing international marketing capability and export performance of emerging market SMEs in crises: strategic flexibility and digital technologies. International Marketing Review, 40(5), 1158-1187.
  • Zhan, Y., Xiong, Y., Han, R., Lam, H. K., & Blome, C. (2024). The impact of artificial intelligence adoption for business-to-business marketing on shareholder reaction: A social actor perspective. International Journal of Information Management, 1-18.
  • Zhong, Y. (2023). E-commerce utilization analysis and growth strategy for smes using an artificial intelligence. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-11.

E-ticareti Benimseyen KOBİ'lerde Yapay Zeka Destekli Pazarlama Yeteneklerinin, Pazarlama Analitiğinin Müşteri Çevikliği ve Pazarlama Performansı Üzerindeki Etkisi

Yıl 2025, Cilt: 11 Sayı: 1, 1 - 14
https://doi.org/10.24289/ijsser.1601570

Öz

Bu çalışma, sosyoloji alanında müfredat çalışmalarına katkıda bulunmuş kadın düşünürleri incelemek amacıyla nitel araştırma yöntemlerinden biri olan doküman analizi tekniği kullanılarak yürütülen bir araştırmadır. Analitik ve yapay zeka uygulamalarının kullanımı, günümüz pazarlama performansını iyileştirmek için bir öncül olarak önemli hale gelmiştir. Bu araştırma, e-ticareti benimseyen işletmelerde pazarlama analitiğinin ve yapay zeka uygulamalarının müşteri çevikliği ve pazarlama performansı üzerindeki etkisini incelemeyi amaçlamaktadır. Bu nicel çalışmada, veriler bir anket yoluyla toplanmıştır. 227 yöneticiden çevrimiçi olarak toplanan veriler Smart PLS yöntemi ile analiz edilmiştir. Çalışma, pazarlama analitiğinin ve yapay zeka destekli pazarlama yeteneklerinin müşteri çevikliğini ve pazarlama performansını etkilediği sonucuna varmıştır. Ayrıca müşteri çevikliğinin pazarlama performansı üzerinde bir etkisi olduğu sonucuna varılmıştır. Ayrıca sonuçlar, müşteri çevikliğinin yapay zeka destekli pazarlama teknolojilerinin ve pazarlama analitiğinin pazarlama performansı üzerindeki etkisinin bir aracı olduğunu göstermektedir. Çalışmanın sınırlılıkları vardır. Gelecekteki araştırmaların çok kültürlü ülkelerden veri toplaması, kültürler arası karşılaştırmalı analizler yürütmesi, daha uzunlamasına bir çalışma yürütmesi ve e-ticareti benimseyen KOBİ'ler için endüstri, süreç, zaman veya ilgili teknoloji gibi değişkenlerin etkilerini daha fazla araştırması önerilmektedir. E-ticareti benimseyen işletmelerin pazarlama analitiğini ve yapay zeka platformlarını nasıl benimsediğini ve bu teknolojilerin müşteri çevikliğine ve pazarlama performansına nasıl katkıda bulunduğunu anlamak faydalı olacaktır. Bu araştırma, dijital pazarlama literatürünü zenginleştirecek ve uygulayıcılara karar alma süreçlerinde rehberlik edecek değişkenleri analiz ederek değer sağlamaktadır.

Kaynakça

  • Agag, G., Shehawy, Y. M., Almoraish, A., Eid, R., Lababdi, H. C., Labben, T. G., & Abdo, S. S. (2024). Understanding the relationship between marketing analytics, customer agility, and customer satisfaction: A longitudinal perspective. Journal of Retailing and Consumer Services, 77, 1-13.
  • Akter, S., Hani, U., Dwivedi, Y. K., & Sharma, A. (2022). The future of marketing analytics in the sharing economy. Industrial Marketing Management, 104, 85-100.
  • Akter, S., Bandara, R. J., & Sajib, S. (2021). How to empower analytics capability to tackle emergency situations?. International Journal of Operations & Production Management, 41(9), 1469-1494.
  • Akter, S., Bandara, R., Hani, U., Wamba, S. F., Foropon, C., & Papadopoulos, T. (2019). Analytics-based decision-making for service systems: A qualitative study and agenda for future research. Int. Journal of Information Management, 48, 85-95.
  • Akter, S., Hossain, M. A., Tarba, S. Y., & Leonidou, E. (2023). How does quality-dominant logic ensure marketing analytics success and tackle business failure in industrial markets?. Industrial Marketing Management, 109, 44-57.
  • Alghamdi, O., & Agag, G. (2024). Competitive advantage: A longitudinal analysis of the roles of data-driven innovation capabilities, marketing agility, and market turbulence. Journal of Retailing and Consumer Services, 76, 1-17.
  • Ali, S., Tian, H., Wu, W., Ali, S., Kumail, T., & Saif, N. (2024). Marketing capabilities, market ambidexterity and product innovation outcomes: A yin-yang of inside-out and outside-in. Industrial Marketing Management, 118, 27-43.
  • Arslan, A., Kamara, S., Tian, A. Y., Rodgers, P., & Kontkanen, M. (2024). Marketing agility in underdog entrepreneurship: A qualitative assessment in post-conflict Sub-Saharan African context. Journal of Business Research, 173, 1-18.
  • Ashrafi, A., Ravasan, A. Z., Trkman, P., & Afshari, S. (2019). The role of business analytics capabilities in bolstering firms' agility and performance. International Journal of Information Management, 47, 1-15.
  • Baabdullah, A. M., Alalwan, A. A., Slade, E. L., Raman, R., & Khatatneh, K. F. (2021). SMEs and artificial intelligence (AI): Antecedents and consequences of AI-based B2B practices. Industrial Marketing Management, 98, 255-270.
  • Barata, S. F., Ferreira, F. A., Carayannis, E. G., & Ferreira, J. J. (2024). Determinants of E-commerce, artificial intelligence, and agile methods in small-and medium-sized enterprises. IEEE Transactions on Engineering Management. 1-12.
  • Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of personality and social psychology, 51(6), 1173.
  • Bawack, R. E., Wamba, S. F., Carillo, K. D. A., & Akter, S. (2022). Artificial intelligence in E-Commerce: a bibliometric study and literature review. Electronic markets, 32(1), 297-338.
  • Bozkurt, S. (2022). The Effect of Perceived Social Media Agility on Customer Engagement Behavior: The Regulatory Role of Social Media Usage Intensity. Social Inventor Academic Review, 3(1), 96-122.
  • Cadden, T., Weerawardena, J., Cao, G., Duan, Y., & McIvor, R. (2023). Examining the role of big data and marketing analytics in SMEs innovation and competitive advantage: A knowledge integration perspective. Journal of Business Research, 168, 1-15.
  • Çallı, B. A., & Çallı, L. (2021). Relationships between digital maturity, organizational agility, and firm performance: an empirical investigation on SMEs. Business & Management Studies: An International Journal, 9(2), 486-502.
  • Cetindas, A. (2023). Information Sharing, Agility and Customer Performance in Supply Chains: A Mediation Model. Turkish Journal of Social Sciences Research, 8(2), 134-145.
  • Chen, D., Esperança, J. P., & Wang, S. (2022). The impact of artificial intelligence on firm performance: an application of the resource-based view to e-commerce firms. Frontiers in Psychology, 13, 1-10.
  • Cheng, C. C., & Shiu, E. C. (2023). The relative values of big data analytics versus traditional marketing analytics to firm innovation: An empirical study. Information & Management, 60(7), 1-9.
  • Clark, B. H. (1999). Marketing performance measures: History and interrelationships. Journal of marketing management, 15(8), 711-732.
  • Demir, Ş. Ş., & Demir, M. (2023). Professionals' perspectives on ChatGPT in the tourism industry: Does it inspire awe or concern?. Journal of Tourism Theory and Research, 9(2), 61-77.
  • Dinç, E. A., & Kazan, H. (2023). Adaptation of Marketing Agility Scale to Turkish (Validity and Reliability Study). International Journal of Management Economics and Business, 19(4), 763-782.
  • Drydakis, N. (2022). Artificial Intelligence and reduced SMEs' business risks. A dynamic capabilities analysis during the COVID-19 pandemic. Information Systems Frontiers, 24(4), 1223-1247.
  • Dwivedi, Y. K., Sharma, A., Rana, N. P., Giannakis, M., Goel, P., & Dutot, V. (2023). Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions. Technological Forecasting and Social Change, 192, 1-9.
  • Fonseka, K., Jaharadak, A. A., & Raman, M. (2022). Impact of E-commerce adoption on business performance of SMEs in Sri Lanka; moderating role of artificial intelligence. International Journal of Social Economics, 49(10), 1518-1531.
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
  • Giacosa, E., Culasso, F., & Crocco, E. (2022). Customer agility in the modern automotive sector: how lead management shapes agile digital companies. Technological Forecasting and Social Change, 175, 1-12.
  • Hadjielias, E., Christofi, M., Christou, P., & Drotarova, M. H. (2022). Digitalization, agility, and customer value in tourism. Technological Forecasting and Social Change, 175, 1-14.
  • Hair Jr, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107-123.
  • Hair, J. F., Ringle, C. M., & Sarstedt, M. (2012). Partial least squares: the better approach to structural equation modeling?. Long range planning, 45(5-6), 312-319.
  • Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial management & data systems, 116(1), 2-20.
  • Hokmabadi, H., Rezvani, S. M., & de Matos, C. A. (2024). Business Resilience for Small and Medium Enterprises and Startups by Digital Transformation and the Role of Marketing Capabilities—A Systematic Review. Systems, 12(6), 220.‏
  • Hossain, M. A., Agnihotri, R., Rushan, M. R. I., Rahman, M. S., & Sumi, S. F. (2022). Marketing analytics capability, artificial intelligence adoption, and firms' competitive advantage: Evidence from the manufacturing industry. Industrial Marketing Management, 106, 240-255.
  • Kamran, H. (2021). The use of artificial intelligence in marketing: A research on consumer acceptance of artificial intelligence marketing tools (Master's thesis, Bursa Uludag University (Turkey)).
  • Khan, A., Talukder, M. S., Islam, Q. T., & Islam, A. N. (2022). The impact of business analytics capabilities on innovation, information quality, agility and firm performance: the moderating role of industry dynamism. VINE Journal of Information and Knowledge Management Systems.1-13.
  • Khan, H. (2020). Is marketing agility important for emerging market firms in advanced markets?. International Business Review, 29(5), 1-13.
  • Kumar, A., Pandey, A., Pujari, P., & Arora, M. (2023). Adoption of AI and e-commerce improving marketing performance of SMEs. Academy of Marketing Studies Journal, 27(5). 1-15.
  • Li, L., Lin, J., Luo, W., & Luo, X. R. (2023). Investigating the effect of artificial intelligence on customer relationship management performance in e-commerce enterprises. Journal of Electronic Commerce Research, 24(1), 68-83.
  • Li, L., Lin, J., Turel, O., Liu, P., & Luo, X. (2020). The impact of e-commerce capabilities on agricultural firms' performance gains: the mediating role of organizational agility. Industrial Management & Data Systems, 120(7), 1265-1286.
  • Liang, X., Li, G., Zhang, H., Nolan, E., & Chen, F. (2022). Firm performance and marketing analytics in the Chinese context: A contingency model. Journal of Business Research, 141, 589-599.
  • Lin, F., & Eng, T. Y. (2024). Entrepreneurial performance and marketing analytics: the role of new product innovation. Journal of Small Business and Enterprise Development. 1-16.
  • Madanchian, M. (2024). The Impact of Artificial Intelligence Marketing on E-Commerce Sales. Systems, 12(10), 429-441.
  • Manis, K. T., & Madhavaram, S. (2023). AI-Supported marketing capabilities and the hierarchy of capabilities: Conceptualization, proposition development, and research avenues. Journal of Business Research, 157, 1-15.
  • Mariani, M. M., Machado, I., & Nambisan, S. (2023). Types of innovation and artificial intelligence: A systematic quantitative literature review and research agenda. Journal of Business Research, 155, 1-14.
  • Masialeti, M., Talaei-Khoei, A., & Yang, A. T. (2024). Revealing the role of explainable AI: How does updating AI applications generate agility-driven performance?. International Journal of Information Management, 77, 1-19.
  • Mehrabi, H., Chen, Y. K., & Keramati, A. (2024). Developing customer analytics capability in firms of different ages: Examining the complementarity of outside-in and inside-out resources. Industrial Marketing Management, 119, 108-121.
  • Ozdemir, S., Wang, Y., Gupta, S., Sena, V., Zhang, S., & Zhang, M. (2024). Customer analytics and new product performance: The role of contingencies. Technological Forecasting and Social Change, 201, 1-15.
  • Peretz-Andersson, E., Tabares, S., Mikalef, P., & Parida, V. (2024). Artificial intelligence implementation in manufacturing SMEs: A resource orchestration approach. International Journal of Information Management, 77, 1-11.
  • Rizvanović, B., Zutshi, A., Grilo, A., & Nodehi, T. (2023). Linking the potentials of extended digital marketing impact and start-up growth: Developing a macro-dynamic framework of start-up growth drivers supported by digital marketing. Technological Forecasting and Social Change, 186, 1-18.
  • Rossi, S., Rossi, M., Mukkamala, R. R., Thatcher, J. B., & Dwivedi, Y. K. (2024). Augmenting research methods with foundation models and generative AI. International Journal of Information Management, 1-9.
  • Salah, O. H., & Ayyash, M. M. (2024). E-commerce adoption by SMEs and its effect on marketing performance: An extended of TOE framework with ai integration, innovation culture, and customer tech-savviness. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 1-13.
  • Schramm-Klein, H., & Morschett, D. (2006). The relationship between marketing performance, logistics performance and company performance for retail companies. International Review of Retail, Distribution and Consumer Research, 16(02), 277-296.
  • Shukla, A., Varshney, J., & Raj, A. (2024). Examining the linkage between managerial ties and firm performance: The mediating role of marketing capabilities and moderation role of industry-A meta-analytic approach. Industrial Marketing Management, 119, 122-134.
  • Tarn, D. D., & Wang, J. (2023). Can data analytics raise marketing agility?-A sense-and-respond perspective. Information & Management, 60(2), 1-13.
  • Tseng, H. T., Aghaali, N., & Hajli, N. (2022). Customer agility and big data analytics in new product context. Technological Forecasting and Social Change, 180, 1-10.
  • Uğurlu, Ö. Y., Çolakoğlu, E., & Öztosun, E. (2019). The effect of strategic agility on firm performance: A research in manufacturing enterprises. Journal of Business and Human, 6(1), 93-106.
  • Wahab, M. D. A., & Radmehr, M. (2024). The impact of AI assimilation on firm performance in small and medium-sized enterprises: A moderated multi-mediation model. Heliyon, 10(8). 1-14.
  • Wamba, S. F. (2022). Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility. International Journal of Information Management, 67, 1-14.
  • Weng, Q., Wang, D., De Lurgio II, S., & Schuetz, S. (2024). How do small-to-medium-sized e-commerce businesses stay competitive? Evidence on the critical roles of IT capability, innovation and multihoming. Internet Research. 1-15.
  • Wu, Q., Yan, D., & Umair, M. (2023). Assessing the role of competitive intelligence and practices of dynamic capabilities in business accommodation of SMEs. Economic Analysis and Policy, 77, 1103-1114.
  • Yaman, T. T., & Bilgiç, E. (2021). The Role of Business Analytics in Strategic Competitiveness of Businesses. Researchgate.
  • Zahoor, N., & Lew, Y. K. (2023). Enhancing international marketing capability and export performance of emerging market SMEs in crises: strategic flexibility and digital technologies. International Marketing Review, 40(5), 1158-1187.
  • Zhan, Y., Xiong, Y., Han, R., Lam, H. K., & Blome, C. (2024). The impact of artificial intelligence adoption for business-to-business marketing on shareholder reaction: A social actor perspective. International Journal of Information Management, 1-18.
  • Zhong, Y. (2023). E-commerce utilization analysis and growth strategy for smes using an artificial intelligence. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-11.
Toplam 64 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Dijital Pazarlama
Bölüm Araştırma Makaleleri
Yazarlar

Fatma Demirağ 0000-0001-7520-6706

Erken Görünüm Tarihi 9 Mart 2025
Yayımlanma Tarihi
Gönderilme Tarihi 14 Aralık 2024
Kabul Tarihi 9 Mart 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 11 Sayı: 1

Kaynak Göster

APA Demirağ, F. (2025). The impact of AI-supported marketing capabilities and analytics on SMEs’ customer agility and marketing performance. International Journal of Social Sciences and Education Research, 11(1), 1-14. https://doi.org/10.24289/ijsser.1601570
AMA Demirağ F. The impact of AI-supported marketing capabilities and analytics on SMEs’ customer agility and marketing performance. International Journal of Social Sciences and Education Research. Mart 2025;11(1):1-14. doi:10.24289/ijsser.1601570
Chicago Demirağ, Fatma. “The Impact of AI-Supported Marketing Capabilities and Analytics on SMEs’ Customer Agility and Marketing Performance”. International Journal of Social Sciences and Education Research 11, sy. 1 (Mart 2025): 1-14. https://doi.org/10.24289/ijsser.1601570.
EndNote Demirağ F (01 Mart 2025) The impact of AI-supported marketing capabilities and analytics on SMEs’ customer agility and marketing performance. International Journal of Social Sciences and Education Research 11 1 1–14.
IEEE F. Demirağ, “The impact of AI-supported marketing capabilities and analytics on SMEs’ customer agility and marketing performance”, International Journal of Social Sciences and Education Research, c. 11, sy. 1, ss. 1–14, 2025, doi: 10.24289/ijsser.1601570.
ISNAD Demirağ, Fatma. “The Impact of AI-Supported Marketing Capabilities and Analytics on SMEs’ Customer Agility and Marketing Performance”. International Journal of Social Sciences and Education Research 11/1 (Mart 2025), 1-14. https://doi.org/10.24289/ijsser.1601570.
JAMA Demirağ F. The impact of AI-supported marketing capabilities and analytics on SMEs’ customer agility and marketing performance. International Journal of Social Sciences and Education Research. 2025;11:1–14.
MLA Demirağ, Fatma. “The Impact of AI-Supported Marketing Capabilities and Analytics on SMEs’ Customer Agility and Marketing Performance”. International Journal of Social Sciences and Education Research, c. 11, sy. 1, 2025, ss. 1-14, doi:10.24289/ijsser.1601570.
Vancouver Demirağ F. The impact of AI-supported marketing capabilities and analytics on SMEs’ customer agility and marketing performance. International Journal of Social Sciences and Education Research. 2025;11(1):1-14.

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