Date Log
Submitted
27 March 2025
Accepted
11 June 2025
Published
22 June 2025
Copyright (c) 2025 Muhammad Syafii A. Basalamah, Regina Regina, Mursalim Laekkeng, Said Hasan (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Streamlining Content Marketing: Antecedents of Productivity in the Age of AI Expansion
Corresponding Author(s) : Muhammad Syafii A. Basalamah
Jurnal Minds: Manajemen Ide dan Inspirasi,
Vol. 12 No. 1 (2025): June
Abstract
The expansion of AI directly affects productivity levels, particularly for content creators, as it alters many aspects of work. This study investigates the impact of AI utilization and content quality on marketing team productivity. A purposive sampling technique employed in this quantitative research distributed an online questionnaire to 200 respondents (content marketing creators). The data is analyzed using structural equation modelling (SEM) with SMART-PLS 4. AI improves content quality and marketing team productivity, whereas content quality boosts productivity. However, AI directly affects team performance. AI is essential for content marketing quality and efficiency. The data used in this study were exclusively derived from questionnaires, which may be subject to limitations due to the subjectivity of respondents or bias in their responses. However, this study can be a foundation for companies and content creators to improve audience personalization and enhance productivity through AI.
Keywords
AI Utilization
Content Quality
Marketing Productivity
AI-Driven Tools
Content Creators
Basalamah, M. S. A., Regina, R., Laekkeng, M., & Hasan, S. (2025). Streamlining Content Marketing: Antecedents of Productivity in the Age of AI Expansion. Jurnal Minds: Manajemen Ide Dan Inspirasi, 12(1), 93–104. https://doi.org/10.24252/minds.v12i1.56310
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References
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Ailloni‐Charas, D. (1993). The Pursuit of Marketing Productivity. Journal of Product & Brand Management, 2(3), 44–48. https://doi.org/10.1108/10610429310046698
Aljarah, A., Ibrahim, B., & López, M. (2024). In AI, we do not trust! The nexus between awareness of falsity in AI-generated CSR ads and online brand engagement. Internet Research, ahead-of-p(ahead-of-print). https://doi.org/10.1108/INTR-12-2023-1156
Alzebda, S., & Matar, M. A. I. (2024). Factors affecting citizen intention toward AI acceptance and adoption: the moderating role of government regulations. Competitiveness Review: An International Business Journal, ahead-of-p(ahead-of-print). https://doi.org/10.1108/CR-06-2023-0144
Berlak, J., Hafner, S., & Kuppelwieser, V. G. (2021). Digitalization impacts productivity: a model-based approach and evaluation in Germany's building construction industry. Production Planning & Control, 32(4), 335–345. https://doi.org/10.1080/09537287.2020.1740815
Chatterjee, S. (2020). AI strategy of India: policy framework, adoption challenges and actions for government. Transforming Government: People, Process and Policy, 14(5), 757–775. https://doi.org/10.1108/TG-05-2019-0031
Chatterjee, S., Chaudhuri, R., Vrontis, D., Thrassou, A., & Ghosh, S. K. (2021). Adoption of artificial intelligence-integrated CRM systems in agile organizations in India. Technological Forecasting and Social Change, 168(April), 120783. https://doi.org/10.1016/j.techfore.2021.120783
Chen, Q., Yin, C., & Gong, Y. (2023). Would an AI chatbot persuade you: an empirical answer from the elaboration likelihood model. Information Technology & People, ahead-of-p(ahead-of-print). https://doi.org/10.1108/ITP-10-2021-0764
Chevalier-Roignant, B., Trigeorgis, L., Chevalier-Roignant, B., & Trigeorgis, L. (2013). Strategic Management and Competitive Advantage. Competitive Strategy, 9780133129304, 47–74. https://doi.org/10.7551/mitpress/9780262015998.003.0002
Chopra, R., Bhardwaj, S., Thaichon, P., & Nair, K. (2024). Unpacking service failures in artificial intelligence: future research directions. Asia Pacific Journal of Marketing and Logistics, ahead-of-p(ahead-of-print). https://doi.org/10.1108/APJML-03-2024-0393
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Eckert, J. A., & Goldsby, T. J. (1997). Using the elaboration likelihood model to guide customer service‐based segmentation. International Journal of Physical Distribution & Logistics Management, 27(9/10), 600–615. https://doi.org/10.1108/09600039710188657
Elhajjar, S. (2024). Unveiling the marketer's lens: exploring experiences and perspectives on AI integration in marketing strategies. Asia Pacific Journal of Marketing and Logistics, ahead-of-p(ahead-of-print). https://doi.org/10.1108/APJML-04-2024-0485
Ghesh, N., Alexander, M., & Davis, A. (2024). The artificial intelligence-enabled customer experience in tourism: a systematic literature review. Tourism Review, 79(5), 1017–1037. https://doi.org/10.1108/TR-04-2023-0255
Good, D. J., & Stone, R. W. (2000). The impact of computerization on marketing performance . Journal of Business & Industrial Marketing, 15(1), 34–56. https://doi.org/10.1108/08858620010311548
Graeme, M., Kofi, O.-F., Alan, W., & Valentina, P. (2020). How live chat assistants drive travel consumers' attitudes, trust and purchase intentions: The role of human touch. International Journal of Contemporary Hospitality Management, 32(5), 1795–1812. https://doi.org/10.1108/IJCHM-07-2019-0605
Güner Gültekin, D., Pinarbasi, F., Yazici, M., & Adiguzel, Z. (2024). Commercialization of artificial intelligence: a research on entrepreneurial companies with challenges and opportunities. Business Process Management Journal, ahead-of-p(ahead-of-print). https://doi.org/10.1108/BPMJ-10-2023-0836
Gupta, Y., & Khan, F. M. (2024). Role of artificial intelligence in customer engagement: a systematic review and future research directions. Journal of Modelling in Management, ahead-of-p(ahead-of-print). https://doi.org/10.1108/JM2-01-2023-0016
Haenlein, M., Kaplan, A., Tan, C. W., & Zhang, P. (2019). Artificial intelligence (AI) and management analytics. Journal of Management Analytics, 6(4), 341–343. https://doi.org/10.1080/23270012.2019.1699876
J. Kitchen, P., Kerr, G., E. Schultz, D., McColl, R., & Pals, H. (2014). The elaboration likelihood model: review, critique and research agenda. European Journal of Marketing, 48(11/12), 2033–2050. https://doi.org/10.1108/EJM-12-2011-0776
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004
Katasonov, A., Veijalainen, J., & Sakkinen, M. (2006). Content quality assessment and acceptance testing in location‐based services. International Journal of Pervasive Computing and Communications, 2(1), 15–34. https://doi.org/10.1108/17427370780000138
Keni, K., Wilson, N., & Teoh, A. P. (2024). Antecedents of viewers' watch behavior toward YouTube videos: evidence from the most populous Muslim-majority country. Journal of Islamic Marketing, 15(2), 446–469. https://doi.org/10.1108/JIMA-01-2023-0008
Kim, J.-S., & Seo, D. (2023). Foresight and strategic decision-making framework from artificial intelligence technology development to utilization activities in small-and-medium-sized enterprises. Foresight, 25(6), 769–787. https://doi.org/10.1108/FS-06-2022-0069
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information and Management, 43(6), 740–755. https://doi.org/10.1016/j.im.2006.05.003
Kumar, D., & Suthar, N. (2024). Ethical and legal challenges of AI in marketing: an exploration of solutions. Journal of Information, Communication and Ethics in Society, 22(1), 124–144. https://doi.org/10.1108/JICES-05-2023-0068
Lee, M., Lee, S. A., Jeong, M., & Oh, H. (2020). Quality of virtual reality and its impacts on behavioral intention. International Journal of Hospitality Management, 90, 102595. https://doi.org/10.1016/J.IJHM.2020.102595
Lin, F., Tian, H., Zhao, J., & Chi, M. (2023). Reward or punish: investigating output controls and content generation in the multi-sided platform context. Internet Research, 33(2), 578–605. https://doi.org/10.1108/INTR-05-2021-0292
Magrath, A. J. (1988). People Productivity: Marketing's Most Valuable Asset. Journal of Business Strategy, 9(4), 12–14. https://doi.org/10.1108/eb039235
Mohammed, A. A. (2019). Using hybrid SEM – artificial intelligence: Approach to examine the nexus between boreout, generation, career, life and job satisfaction. Personnel Review, 49(1), 67–86. https://doi.org/10.1108/PR-06-2017-0180
Peltier, J. W., Dahl, A. J., & Schibrowsky, J. A. (2024). Artificial intelligence in interactive marketing: a conceptual framework and research agenda. Journal of Research in Interactive Marketing, 18(1), 54–90. https://doi.org/10.1108/JRIM-01-2023-0030
Ratna, S., Saide, S., Putri, A. M., Indrajit, R. E., & Muwardi, D. (2024). Digital transformation in tourism and hospitality industry: a literature review of blockchain, financial technology, and knowledge management. EuroMed Journal of Business, 19(1), 84–112. https://doi.org/10.1108/EMJB-04-2023-0118
Robinson, O. C., & Robinson, O. C. (2016). Qualitative Research in Psychology Sampling in Interview-Based Qualitative Research : A Theoretical and Practical Guide A Theoretical and Practical Guide. Qualitative Research in Psychology, in Press, 0887(February), 1–25.
Santoro, G., Jabeen, F., Kliestik, T., & Bresciani, S. (2024). AI-powered growth hacking: benefits, challenges and pathways. Management Decision, ahead-of-p(ahead-of-print). https://doi.org/10.1108/MD-10-2023-1964
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial Least Squares Structural Equation Modeling. Handbook of Market Research, 1–47. https://doi.org/10.1007/978-3-319-05542-8_15-2
Serge-Lopez, W.-T., Samuel, F. W., Robert, K. K. J., & Emmanuel, T. W. C. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. In Business Process Management Journal: Vol. ahead-of-p (Issue ahead-of-print). https://doi.org/10.1108/BPMJ-10-2019-0411
Sharma, P. N., Liengaard, B. D., Hair, J. F., Sarstedt, M., & Ringle, C. M. (2023). Predictive model assessment and selection in composite-based modelling using PLS-SEM: extensions and guidelines for using CVPAT. European Journal of Marketing, 57(6), 1662–1677. https://doi.org/10.1108/EJM-08-2020-0636
Wang, X., & Cheng, Z. (2020). Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations. Chest, 158(1), S65–S71. https://doi.org/10.1016/j.chest.2020.03.012
We Are Social. (2023). Digital 2023 Indonesia. We Are Social, 125. https://wearesocial.com/wp-content/uploads/2023/03/Digital-2023-Indonesia.pdf
Wood, D., & Moss, S. H. (2024). Evaluating the impact of students' generative AI use in educational contexts. Journal of Research in Innovative Teaching and Learning, 17(2), 152–167. https://doi.org/10.1108/JRIT-06-2024-0151
Yaqub, M. Z., Badghish, S., Yaqub, R. M. S., Ali, I., & Ali, N. S. (2024). Integrating and extending the SOR model, TAM and the UTAUT to assess M-commerce adoption during COVID times. Journal of Economic and Administrative Sciences, ahead-of-p(ahead-of-print). https://doi.org/10.1108/JEAS-09-2023-0259
Yoo, B., Katsumata, S., & Ichikohji, T. (2019). The impact of customer orientation on the quantity and quality of user-generated content. Asia Pacific Journal of Marketing and Logistics, 31(2), 516–540. https://doi.org/10.1108/APJML-03-2018-0118
Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: a meta‐analysis of the TAM: Part 1. Journal of Modelling in Management, 2(3), 251–280. https://doi.org/10.1108/17465660710834453
References
Ailloni‐Charas, D. (1993). The Pursuit of Marketing Productivity. Journal of Product & Brand Management, 2(3), 44–48. https://doi.org/10.1108/10610429310046698
Aljarah, A., Ibrahim, B., & López, M. (2024). In AI, we do not trust! The nexus between awareness of falsity in AI-generated CSR ads and online brand engagement. Internet Research, ahead-of-p(ahead-of-print). https://doi.org/10.1108/INTR-12-2023-1156
Alzebda, S., & Matar, M. A. I. (2024). Factors affecting citizen intention toward AI acceptance and adoption: the moderating role of government regulations. Competitiveness Review: An International Business Journal, ahead-of-p(ahead-of-print). https://doi.org/10.1108/CR-06-2023-0144
Berlak, J., Hafner, S., & Kuppelwieser, V. G. (2021). Digitalization impacts productivity: a model-based approach and evaluation in Germany's building construction industry. Production Planning & Control, 32(4), 335–345. https://doi.org/10.1080/09537287.2020.1740815
Chatterjee, S. (2020). AI strategy of India: policy framework, adoption challenges and actions for government. Transforming Government: People, Process and Policy, 14(5), 757–775. https://doi.org/10.1108/TG-05-2019-0031
Chatterjee, S., Chaudhuri, R., Vrontis, D., Thrassou, A., & Ghosh, S. K. (2021). Adoption of artificial intelligence-integrated CRM systems in agile organizations in India. Technological Forecasting and Social Change, 168(April), 120783. https://doi.org/10.1016/j.techfore.2021.120783
Chen, Q., Yin, C., & Gong, Y. (2023). Would an AI chatbot persuade you: an empirical answer from the elaboration likelihood model. Information Technology & People, ahead-of-p(ahead-of-print). https://doi.org/10.1108/ITP-10-2021-0764
Chevalier-Roignant, B., Trigeorgis, L., Chevalier-Roignant, B., & Trigeorgis, L. (2013). Strategic Management and Competitive Advantage. Competitive Strategy, 9780133129304, 47–74. https://doi.org/10.7551/mitpress/9780262015998.003.0002
Chopra, R., Bhardwaj, S., Thaichon, P., & Nair, K. (2024). Unpacking service failures in artificial intelligence: future research directions. Asia Pacific Journal of Marketing and Logistics, ahead-of-p(ahead-of-print). https://doi.org/10.1108/APJML-03-2024-0393
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Eckert, J. A., & Goldsby, T. J. (1997). Using the elaboration likelihood model to guide customer service‐based segmentation. International Journal of Physical Distribution & Logistics Management, 27(9/10), 600–615. https://doi.org/10.1108/09600039710188657
Elhajjar, S. (2024). Unveiling the marketer's lens: exploring experiences and perspectives on AI integration in marketing strategies. Asia Pacific Journal of Marketing and Logistics, ahead-of-p(ahead-of-print). https://doi.org/10.1108/APJML-04-2024-0485
Ghesh, N., Alexander, M., & Davis, A. (2024). The artificial intelligence-enabled customer experience in tourism: a systematic literature review. Tourism Review, 79(5), 1017–1037. https://doi.org/10.1108/TR-04-2023-0255
Good, D. J., & Stone, R. W. (2000). The impact of computerization on marketing performance . Journal of Business & Industrial Marketing, 15(1), 34–56. https://doi.org/10.1108/08858620010311548
Graeme, M., Kofi, O.-F., Alan, W., & Valentina, P. (2020). How live chat assistants drive travel consumers' attitudes, trust and purchase intentions: The role of human touch. International Journal of Contemporary Hospitality Management, 32(5), 1795–1812. https://doi.org/10.1108/IJCHM-07-2019-0605
Güner Gültekin, D., Pinarbasi, F., Yazici, M., & Adiguzel, Z. (2024). Commercialization of artificial intelligence: a research on entrepreneurial companies with challenges and opportunities. Business Process Management Journal, ahead-of-p(ahead-of-print). https://doi.org/10.1108/BPMJ-10-2023-0836
Gupta, Y., & Khan, F. M. (2024). Role of artificial intelligence in customer engagement: a systematic review and future research directions. Journal of Modelling in Management, ahead-of-p(ahead-of-print). https://doi.org/10.1108/JM2-01-2023-0016
Haenlein, M., Kaplan, A., Tan, C. W., & Zhang, P. (2019). Artificial intelligence (AI) and management analytics. Journal of Management Analytics, 6(4), 341–343. https://doi.org/10.1080/23270012.2019.1699876
J. Kitchen, P., Kerr, G., E. Schultz, D., McColl, R., & Pals, H. (2014). The elaboration likelihood model: review, critique and research agenda. European Journal of Marketing, 48(11/12), 2033–2050. https://doi.org/10.1108/EJM-12-2011-0776
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004
Katasonov, A., Veijalainen, J., & Sakkinen, M. (2006). Content quality assessment and acceptance testing in location‐based services. International Journal of Pervasive Computing and Communications, 2(1), 15–34. https://doi.org/10.1108/17427370780000138
Keni, K., Wilson, N., & Teoh, A. P. (2024). Antecedents of viewers' watch behavior toward YouTube videos: evidence from the most populous Muslim-majority country. Journal of Islamic Marketing, 15(2), 446–469. https://doi.org/10.1108/JIMA-01-2023-0008
Kim, J.-S., & Seo, D. (2023). Foresight and strategic decision-making framework from artificial intelligence technology development to utilization activities in small-and-medium-sized enterprises. Foresight, 25(6), 769–787. https://doi.org/10.1108/FS-06-2022-0069
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information and Management, 43(6), 740–755. https://doi.org/10.1016/j.im.2006.05.003
Kumar, D., & Suthar, N. (2024). Ethical and legal challenges of AI in marketing: an exploration of solutions. Journal of Information, Communication and Ethics in Society, 22(1), 124–144. https://doi.org/10.1108/JICES-05-2023-0068
Lee, M., Lee, S. A., Jeong, M., & Oh, H. (2020). Quality of virtual reality and its impacts on behavioral intention. International Journal of Hospitality Management, 90, 102595. https://doi.org/10.1016/J.IJHM.2020.102595
Lin, F., Tian, H., Zhao, J., & Chi, M. (2023). Reward or punish: investigating output controls and content generation in the multi-sided platform context. Internet Research, 33(2), 578–605. https://doi.org/10.1108/INTR-05-2021-0292
Magrath, A. J. (1988). People Productivity: Marketing's Most Valuable Asset. Journal of Business Strategy, 9(4), 12–14. https://doi.org/10.1108/eb039235
Mohammed, A. A. (2019). Using hybrid SEM – artificial intelligence: Approach to examine the nexus between boreout, generation, career, life and job satisfaction. Personnel Review, 49(1), 67–86. https://doi.org/10.1108/PR-06-2017-0180
Peltier, J. W., Dahl, A. J., & Schibrowsky, J. A. (2024). Artificial intelligence in interactive marketing: a conceptual framework and research agenda. Journal of Research in Interactive Marketing, 18(1), 54–90. https://doi.org/10.1108/JRIM-01-2023-0030
Ratna, S., Saide, S., Putri, A. M., Indrajit, R. E., & Muwardi, D. (2024). Digital transformation in tourism and hospitality industry: a literature review of blockchain, financial technology, and knowledge management. EuroMed Journal of Business, 19(1), 84–112. https://doi.org/10.1108/EMJB-04-2023-0118
Robinson, O. C., & Robinson, O. C. (2016). Qualitative Research in Psychology Sampling in Interview-Based Qualitative Research : A Theoretical and Practical Guide A Theoretical and Practical Guide. Qualitative Research in Psychology, in Press, 0887(February), 1–25.
Santoro, G., Jabeen, F., Kliestik, T., & Bresciani, S. (2024). AI-powered growth hacking: benefits, challenges and pathways. Management Decision, ahead-of-p(ahead-of-print). https://doi.org/10.1108/MD-10-2023-1964
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial Least Squares Structural Equation Modeling. Handbook of Market Research, 1–47. https://doi.org/10.1007/978-3-319-05542-8_15-2
Serge-Lopez, W.-T., Samuel, F. W., Robert, K. K. J., & Emmanuel, T. W. C. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. In Business Process Management Journal: Vol. ahead-of-p (Issue ahead-of-print). https://doi.org/10.1108/BPMJ-10-2019-0411
Sharma, P. N., Liengaard, B. D., Hair, J. F., Sarstedt, M., & Ringle, C. M. (2023). Predictive model assessment and selection in composite-based modelling using PLS-SEM: extensions and guidelines for using CVPAT. European Journal of Marketing, 57(6), 1662–1677. https://doi.org/10.1108/EJM-08-2020-0636
Wang, X., & Cheng, Z. (2020). Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations. Chest, 158(1), S65–S71. https://doi.org/10.1016/j.chest.2020.03.012
We Are Social. (2023). Digital 2023 Indonesia. We Are Social, 125. https://wearesocial.com/wp-content/uploads/2023/03/Digital-2023-Indonesia.pdf
Wood, D., & Moss, S. H. (2024). Evaluating the impact of students' generative AI use in educational contexts. Journal of Research in Innovative Teaching and Learning, 17(2), 152–167. https://doi.org/10.1108/JRIT-06-2024-0151
Yaqub, M. Z., Badghish, S., Yaqub, R. M. S., Ali, I., & Ali, N. S. (2024). Integrating and extending the SOR model, TAM and the UTAUT to assess M-commerce adoption during COVID times. Journal of Economic and Administrative Sciences, ahead-of-p(ahead-of-print). https://doi.org/10.1108/JEAS-09-2023-0259
Yoo, B., Katsumata, S., & Ichikohji, T. (2019). The impact of customer orientation on the quantity and quality of user-generated content. Asia Pacific Journal of Marketing and Logistics, 31(2), 516–540. https://doi.org/10.1108/APJML-03-2018-0118
Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: a meta‐analysis of the TAM: Part 1. Journal of Modelling in Management, 2(3), 251–280. https://doi.org/10.1108/17465660710834453
Aljarah, A., Ibrahim, B., & López, M. (2024). In AI, we do not trust! The nexus between awareness of falsity in AI-generated CSR ads and online brand engagement. Internet Research, ahead-of-p(ahead-of-print). https://doi.org/10.1108/INTR-12-2023-1156
Alzebda, S., & Matar, M. A. I. (2024). Factors affecting citizen intention toward AI acceptance and adoption: the moderating role of government regulations. Competitiveness Review: An International Business Journal, ahead-of-p(ahead-of-print). https://doi.org/10.1108/CR-06-2023-0144
Berlak, J., Hafner, S., & Kuppelwieser, V. G. (2021). Digitalization impacts productivity: a model-based approach and evaluation in Germany's building construction industry. Production Planning & Control, 32(4), 335–345. https://doi.org/10.1080/09537287.2020.1740815
Chatterjee, S. (2020). AI strategy of India: policy framework, adoption challenges and actions for government. Transforming Government: People, Process and Policy, 14(5), 757–775. https://doi.org/10.1108/TG-05-2019-0031
Chatterjee, S., Chaudhuri, R., Vrontis, D., Thrassou, A., & Ghosh, S. K. (2021). Adoption of artificial intelligence-integrated CRM systems in agile organizations in India. Technological Forecasting and Social Change, 168(April), 120783. https://doi.org/10.1016/j.techfore.2021.120783
Chen, Q., Yin, C., & Gong, Y. (2023). Would an AI chatbot persuade you: an empirical answer from the elaboration likelihood model. Information Technology & People, ahead-of-p(ahead-of-print). https://doi.org/10.1108/ITP-10-2021-0764
Chevalier-Roignant, B., Trigeorgis, L., Chevalier-Roignant, B., & Trigeorgis, L. (2013). Strategic Management and Competitive Advantage. Competitive Strategy, 9780133129304, 47–74. https://doi.org/10.7551/mitpress/9780262015998.003.0002
Chopra, R., Bhardwaj, S., Thaichon, P., & Nair, K. (2024). Unpacking service failures in artificial intelligence: future research directions. Asia Pacific Journal of Marketing and Logistics, ahead-of-p(ahead-of-print). https://doi.org/10.1108/APJML-03-2024-0393
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Eckert, J. A., & Goldsby, T. J. (1997). Using the elaboration likelihood model to guide customer service‐based segmentation. International Journal of Physical Distribution & Logistics Management, 27(9/10), 600–615. https://doi.org/10.1108/09600039710188657
Elhajjar, S. (2024). Unveiling the marketer's lens: exploring experiences and perspectives on AI integration in marketing strategies. Asia Pacific Journal of Marketing and Logistics, ahead-of-p(ahead-of-print). https://doi.org/10.1108/APJML-04-2024-0485
Ghesh, N., Alexander, M., & Davis, A. (2024). The artificial intelligence-enabled customer experience in tourism: a systematic literature review. Tourism Review, 79(5), 1017–1037. https://doi.org/10.1108/TR-04-2023-0255
Good, D. J., & Stone, R. W. (2000). The impact of computerization on marketing performance . Journal of Business & Industrial Marketing, 15(1), 34–56. https://doi.org/10.1108/08858620010311548
Graeme, M., Kofi, O.-F., Alan, W., & Valentina, P. (2020). How live chat assistants drive travel consumers' attitudes, trust and purchase intentions: The role of human touch. International Journal of Contemporary Hospitality Management, 32(5), 1795–1812. https://doi.org/10.1108/IJCHM-07-2019-0605
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