Harnessing technological resources for effective growth hacking: A mixed-method framework using systematic literature review, content analysis, and multi-layer decision-Making
通过系统文献综述和内容分析识别增长黑客特征，用贝叶斯最优最劣法评估重要性，并建立数学模型找出关键智能技术，发现大数据和人工智能对增长黑客影响最大。
The rise of Industry 4.0′s digital transformations has revolutionised organisational practices and significantly influenced analysis methods. One effective strategy affected by smart technologies is growth hacking. Growth hacking equips organisations with skills in product enhancement and customer acquisition tools, drastically enhancing efficiency and effectiveness. It strengthens organisations and accelerates growth through agile processes, enabling them to maintain competitive advantages. This study aims to identify and analyse technological resources and their impacts on growth hacking features to familiarise organisations and adopt agile strategies based on learning and creativity. Using a mixed-method approach, a systematic literature review (SLR) and content analysis (CA) uncover growth hacking and smart technology features. The Bayesian best-worst method (BBWM) assesses their importance, while a set-covering based mathematical model identifies key smart technologies that bolster growth hacking features. Accordingly, the growth hacking approach includes seven features, with innovation and creativity being the most important. Furthermore, it was revealed that Big Data and Artificial Intelligence are among the most important technologies impacting the growth hacking features. Interestingly, artificial intelligence has the potential to promote all features and increase the efficiency and speed of analysis in growth hacking.