دانلود مقاله شایستگی های AI برای عملکرد سازمانی: دیدگاه قابلیت های بازاریابی B2B
خلاصه
1. معرفی
2. پس زمینه
3. مدل و فرضیه های تحقیق
4. مطالعه تجربی
5. تجزیه و تحلیل
6. بحث
بیانیه مشارکت نویسنده CRediT
اعلامیه منافع رقابتی
سپاسگزاریها
پیوست اول. . پرسشنامه
منابع
Abstract
1. Introduction
2. Background
3. Research model and hypotheses
4. Empirical study
5. Analysis
6. Discussion
CRediT authorship contribution statement
Declaration of Competing Interest
Acknowledgements
Appendix A. . Questionnaire
References
چکیده
استقرار هوش مصنوعی (AI) در چند سال گذشته در چندین زمینه شتاب گرفته است و تمرکز زیادی روی پتانسیل آن در بازاریابی کسب و کار به تجارت (B2B) گذاشته شده است. گزارش‌های اولیه مزایای امیدوارکننده هوش مصنوعی در بازاریابی B2B مانند ارائه بینش‌های مهم در مورد رفتارهای مشتری، شناسایی بینش مهم بازار، و ساده‌سازی ناکارآمدی‌های عملیاتی را برجسته می‌کنند. با این وجود، درک درستی در مورد اینکه سازمان‌ها چگونه باید شایستگی‌های هوش مصنوعی خود را برای بازاریابی B2B ساختاردهی کنند، و اینکه چگونه اینها در نهایت بر عملکرد سازمانی تأثیر می‌گذارند، وجود ندارد. این مطالعه با تکیه بر شایستگی‌های هوش مصنوعی و ادبیات بازاریابی B2B، یک مدل تحقیقاتی مفهومی را توسعه می‌دهد که تأثیر شایستگی‌های هوش مصنوعی بر قابلیت‌های بازاریابی B2B و به نوبه خود بر عملکرد سازمانی را بررسی می‌کند. مدل تحقیق پیشنهادی با استفاده از 155 پاسخ نظرسنجی از شرکت‌های اروپایی آزمایش شده و با استفاده از مدل‌سازی معادلات ساختاری حداقل مربعات جزئی تجزیه و تحلیل شده است. نتایج مکانیسم‌هایی را که از طریق آن شایستگی‌های هوش مصنوعی بر قابلیت‌های بازاریابی B2B تأثیر می‌گذارد، و همچنین چگونگی تأثیر بعدی بر عملکرد سازمانی را نشان می‌دهد.
Abstract
The deployment of Artificial Intelligence (AI) has been accelerating in several fields over the past few years, with much focus placed on its potential in Business-to-Business (B2B) marketing. Early reports highlight promising benefits of AI in B2B marketing such as offering important insights into customer behaviors, identifying critical market insight, and streamlining operational inefficiencies. Nevertheless, there is a lack of understanding concerning how organizations should structure their AI competencies for B2B marketing, and how these ultimately influence organizational performance. Drawing on AI competencies and B2B marketing literature, this study develops a conceptual research model that explores the effect that AI competencies have on B2B marketing capabilities, and in turn on organizational performance. The proposed research model is tested using 155 survey responses from European companies and analyzed using partial least squares structural equation modeling. The results highlight the mechanisms through which AI competencies influence B2B marketing capabilities, as well as how the later impact organizational performance.
Introduction
The deluge of data combine with the availability of processing power and storage on digital devices has created a renewed interest in artificial intelligence (AI) in multiple fields over the past years (Enholm et al., 2021). Intense competition among organizations all over the world has also accelerated the need to deploy AI in order to gain an edge over rivals (Ransbotham et al., 2018). AI is not perceived by most C-level executives as a core competence that organizations must foster to remain competitive in the long-run (Kietzmann & Pitt, 2020). One key area of AI use within organizational operations has been B2B marketing (Mikalef et al., 2021). Intelligent solutions to augment B2B marketing capabilities are necessary in a complex business environment, as B2B operations often deal with massive informational complexity and the requirement to make quick decisions. In this regard, AI has the potential to revolutionize how conventional activities are performed due to the ability to process ever-increasing volumes of data, and provide rich insights on key business partners and customers (Bag et al., 2021). Furthermore, AI applications have been suggested to enable automation of many manual processes which can help alleviate bottlenecks and increase operational efficiency in B2B operations (Paschen et al., 2020). In fact, a recent survey on business executives conducted by Garner indicated that the majority believe that AI is likely to be a key development in their business within the next years (Shin & Kang, 2022).
Analysis
To examine the validity and reliability of our proposed research model, we built on a partial least square based structural equation modeling (PLS-SEM) analysis. We used SmartPLS 4 as the software to run analyses (Ringle et al., 2015). For the type of analysis we conduct PLS-SEM is deemed as an appropriate technique as it allows the examination of the relationships between dependent, independent, and mediating variables (Hair et al., 2011). As PLS-SEM is a variance-based approach, it allows for (i) flexibility concerning normality, (ii) use of reflective and formative constructs, (iii) analysis of models with smaller samples, and (iv) the potential of theory building (Nair et al., 2017). Over the past years, PLS-SEM has widely been used for the analysis of models with complex relationships between constructs in several subject areas (Ahammad et al., 2017, West et al., 2016). Furthermore, PLS-SEM enables the identification of indirect and total effects, which makes it making it possible to not only simultaneously assess the relationships between multi-item constructs, but also to reduce the overall error associated with the model (Astrachan et al., 2014). The sample of 155 responses of this study surpasses both the requirements of: (1) ten times the largest number of formative indicators used to measure one construct, and (2) ten times the largest number of structural paths directed at a particular latent construct in the structural model (Hair et al., 2011). Furthermore, as the research model we examine builds on exploratory theory building instead of confirming, we consider that PLS-SEM is the best alternative.