دانلود مقاله پذیرش سیستم های CRM ادغام یافته با هوش مصنوعی
خلاصه
1. مقدمه - پیشینه تحقیق، شکاف و هدف
2. مبانی نظری و زمینه سازی
3. توسعه و مفهوم سازی فرضیه ها
4. روش تحقیق و نتایج
5. یافته ها و مفاهیم
6. اظهارات پایانی، محدودیت ها، و راه های تحقیقات آینده
ضمیمه
منابع
Abstract
1. Introduction – research background, gap and aim
2. Theoretical foundations and contextualization
3. Developing and conceptualizing hypotheses
4. Research methodology and results
5. Findings and implications
6. Concluding remarks, limitations, and future research avenues
Appendix
References
چکیده
هوش مصنوعی ادغام شده با سیستم های مدیریت ارتباط با مشتری (CRM) ابزارهای سازمان ها را برای تجزیه و تحلیل حجم عظیمی از داده های مشتری متحول کرده است. برای پاسخگویی و مدیریت مؤثر فرصت‌ها و چالش‌های ناشی از این امر، سازمان‌ها در حال توسعه شایستگی‌ها و فرآیندهایی هستند که چابکی آنها را تکامل می‌دهد و آنها را با سیستم خدمات مشتری هوش مصنوعی (AICS) و تنظیمات دیجیتالی‌سازی گسترده‌تر تنظیم می‌کند. در این زمینه، این مطالعه عوامل موثر بر پذیرش یک سیستم CRM یکپارچه با هوش مصنوعی (AICS) در سازمان‌های چابک را به عنوان بخشی از استراتژی دیجیتالی‌سازی آنها شناسایی می‌کند. از نظر روش‌شناسی، این تحقیق پایه‌های نظری خود را بر روی آثار موجود برای توسعه فرضیه‌ها و مدل مفهومی مربوطه بنا می‌کند. این مدل از نظر کمی از طریق یک نظرسنجی در سراسر طیف شرکت‌های هندی، پس از پیش‌آزمون مبتنی بر متخصص و آزمایش آزمایشی، تأیید می‌شود و متعاقباً با استفاده از روش مدل‌سازی معادلات ساختاری حداقل مربعات جزئی (PLS-SEM) آزمایش آماری می‌شود. نتایج، که در پس زمینه چابکی سازمانی ارائه شده است، رابطه بین ذینفعان و ارزش و سهولت درک شده AICS، بین اعتماد و نگرش کارکنان، و تأثیر نگرش و قصد رفتاری را به عنوان میانجی های کلیدی در پذیرش AICS شناسایی و روشن می کند. یافته‌ها به طور قطعی به پیامدهای ملموس برای تمرین و راه‌های صریح برای تحقیقات آتی تبدیل می‌شوند.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
Artificial intelligence integrated with customer relationship management (CRM) systems has revolutionized organizations’ means of analyzing their huge volumes of customer data. To effectively respond to and manage the opportunities and challenges that arise from this, organizations are developing competencies and processes that evolve their agility, fine-tuning them to the artificial intelligence customer service system (AICS) and wider digitalization setting. In this context, this study identifies the factors impacting the adoption of an AI-integrated CRM system (AICS) in agile organizations as a part of their digitalization strategy. Methodologically, the research builds its theoretical foundation on extant works to develop hypotheses and a corresponding conceptual model. The model is quantitatively validated through a survey across the spectrum of Indian companies, following expert-based pretesting and pilot testing, and subsequently it is statistically tested using the partial least squares structural equation modeling (PLS-SEM) technique. The results, contextualized against the backdrop of organizational agility, identify and elucidate the relationship between stakeholders and perceived value and easiness of AICS, between employee trust and attitude, and the influence of attitude and behavioral intention as key mediators towards AICS adoption. The findings are conclusively transcribed into tangible implications for practice and explicit avenues for future research.
Introduction
This research is grounded in three unequivocal premises stemming from contemporary organizational realities and inescapable contextual business precincts. These are the customer-centric nature of business (focus), the increasing dependence of organizational operations on technology (means), and the ever-changing and unpredictable nature of the business environment that demands attitudinal and procedural agility to adapt swiftly and effectively to the changes for competitive advantage (aim). For the purposes of this research, these are practicably and generally transcribed into customer relationship management (CRM), digitalization and artificial intelligence (AI) systems, and applied agility. Thus, this paper studies artificial intelligence-integrated customer relationship management systems, in the context of organizational agility, as the intersection of the three most prominent notions – CRM, digitalization, and AI – of present business theory and practice, and with corresponding value and contribution to knowledge. Accurate analysis of customer data is an important CRM activity. Organizations perform such activities to extract the best information. But since the volume of customer data can be huge, we need to investigate how AI technology could manage and analyze so much data in an accurate and cost-effective way to achieve business success (Gnizy, 2019). Only using AI in a CRM system might suffice in a static business environment. However, since the business environment is ever-changing and unpredictable in nature, organizations should have apt procedural and attitudinal agility to quickly adapt to the changes to gain competitive advantage. Hence, we need to know how the intersection of agility and AICS could fetch competitive advantage for the organizations.
Findings and implications
The agility contextualization
As already highlighted, extracting the full potential of AICS in organizations is also linked to the organization’s agile competencies. However, the true question arising from this is how should the organization be agile; in other words, what is the nature of this relationship and how can it be optimized? Agility, in this context, should not be considered a stand-alone capability but rather viewed as a collection of different specific competencies. These include adaptability, innovation, resilience, and sustainability (Holbeche, 2018). The study highlights that if the stakeholders of the organizations could understand the usefulness of AICS and feel that AICS is not that complex to use, they would be inclined to use it. For this to happen, the organization must be agile in extracting the potential of AICS by addressing the threats and challenges and by utilizing its capabilities in the best possible way (Overby, 2006). The organizations should be agile to structure their IT competencies to sense and respond to threats and uncertainty, which would develop stakeholders’ trust. The validated results show that PU impacts BI (β = 0.81, p < 0.01), whereas PEU impacts PU (β = 0.80, p < 0.01), and earlier studies (Venkatesh and Davis, 2000) have also supported these linkages. Validation shows that trust impacts attitude, and intention impacts adoption, which earlier studies (DeConick, 2010) also supported. It appears that the impacts of PU on ATT and TR on AICS are insignificant, contradicting some earlier studies. The possible reasons for such deviation and contradiction have been explained earlier in detail. This study identified the antecedents that predict attitude, intention, and adoption after studying different models and by subsequently conducting statistical validation of the conceptual model. Fig. 3 helps us to provide the relevant regression equations, which are shown below.