The purpose of this research is to examine the impact of artificial intelligence (AI) on customer performance and identify the factors contributing to its effectiveness by employing a quantitative approach, specifically the partial least squares method, to test the hypotheses and explore the relationships between various variables. The findings indicate that effective business practices and successful AI assimilation have a positive impact on customer performance. Additionally, the results of this study provide valuable insights for both academic and practical communities. This study highlights the importance of specific variables, such as organizational and customer agility, customer experience, customer relationship quality, and customer performance in AI assimilation. By exploring these variables, it contributes significantly to the academic, managerial, and social aspects of AI and its impact on customer performance.
本研究的目的是检验人工智能 (AI) 对客户绩效的影响，并通过采用定量方法（特别是偏最小二乘法）来确定影响其有效性的因素，以检验假设并探索各种因素之间的关系。变量。研究结果表明，有效的业务实践和成功的人工智能同化对客户绩效产生积极影响。此外，这项研究的结果为学术界和实践界提供了宝贵的见解。这项研究强调了特定变量的重要性，例如组织和客户敏捷性、客户体验、客户关系质量和客户绩效在人工智能同化中的重要性。通过探索这些变量，它对人工智能的学术、管理和社会方面及其对客户绩效的影响做出了重大贡献。
1. Introduction 一、简介
The theory of Artificial Intelligence (AI) integration emphasizes the use of AI technology to optimize an existing system or process. AI is a technology that can automatically process data or information using predefined algorithms (Jiang, 2020; Kineber et al., 2023). The theory of AI technology integration is crucial for understanding because AI technology can provide numerous benefits for the development of a system or process. By employing AI technology, systems or processes can operate more efficiently and swiftly, thereby enhancing their productivity and quality. Furthermore, AI technology can assist in reducing the risk of human error, which is often encountered in various processes (Sanchez-Franco et al., 2019).
人工智能（AI）集成理论强调利用人工智能技术来优化现有系统或流程。人工智能是一种可以使用预定义算法自动处理数据或信息的技术（Jiang，2020；Kineber 等，2023）。人工智能技术集成理论对于理解至关重要，因为人工智能技术可以为系统或流程的开发提供众多好处。通过采用人工智能技术，系统或流程可以更高效、更迅速地运行，从而提高生产力和质量。此外，人工智能技术可以帮助降低各种流程中经常遇到的人为错误的风险（Sanchez-Franco 等人，2019）。
The influence of AI in the modern world is evident across various industries, including healthcare and finance. According to Accenture’s report, the global AI healthcare market is projected to reach $6.6 billion in 2021, growing at a CAGR of 40% (Al-Shoteri, 2022). The use of AI technology in healthcare enhances patient outcomes by analyzing large volumes of medical data and identifying patterns that can be used to develop personalized treatment plans. In the financial industry, AI is used to improve fraud detection, automate financial processes, and enhance customer service. A report from CB Insights indicates that AI startups in the financial industry raised $5.4 billion in funding in 2020 (da Costa et al., 2022). These examples not only demonstrate the potential benefits of AI across various industries but also underscore the importance of addressing issues such as data security and privacy.
人工智能对现代世界的影响在各个行业都很明显，包括医疗保健和金融。根据埃森哲的报告，全球人工智能医疗保健市场预计到 2021 年将达到 66 亿美元，复合年增长率为 40%（Al-Shoteri，2022）。人工智能技术在医疗保健中的使用通过分析大量医疗数据并识别可用于制定个性化治疗计划的模式来改善患者的治疗效果。在金融行业，人工智能用于改进欺诈检测、自动化财务流程并增强客户服务。 CB Insights 的一份报告显示，金融行业的人工智能初创企业在 2020 年筹集了 54 亿美元的资金（da Costa 等人，2022）。这些例子不仅展示了人工智能在各个行业的潜在好处，还强调了解决数据安全和隐私等问题的重要性。
Additionally, the theory of AI technology integration is crucial for enhancing the ability of a system or process to analyze and predict data or information. With AI technology, systems or processes can efficiently process large amounts of data quickly and accurately, aiding informed decisions in various situations (Al-Jedibi, 2022; Al-Shoteri, 2022; Najmi et al., 2021). However, before implementing AI technology in a system or process, data security, privacy, and the capability of AI technology to process high-quality data should be considered. Therefore, careful planning and proper training are necessary to effectively and correctly utilize AI technology in systems or processes (Hitoshi, 2021).
此外，人工智能技术集成理论对于增强系统或过程分析和预测数据或信息的能力至关重要。借助人工智能技术，系统或流程可以快速、准确地高效处理大量数据，帮助在各种情况下做出明智的决策（Al-Jedibi，2022；Al-Shoteri，2022；Najmi 等人，2021）。然而，在系统或流程中实施人工智能技术之前，应考虑数据安全、隐私以及人工智能技术处理高质量数据的能力。因此，为了在系统或流程中有效和正确地利用人工智能技术，需要仔细规划和适当的培训（Hitoshi，2021）。
It is important to remember that AI technology cannot entirely replace human roles. Although it can assist in optimizing a system or process, humans remain irreplaceable in the decision-making and control of systems or processes (Rezaei, 2015). The assimilation of AI into customer performance combines AI technology with customer services to enhance customer performance and satisfaction. With AI, companies can gather and analyze customer data to determine customer needs and preferences more accurately.
重要的是要记住，人工智能技术不能完全取代人类的角色。尽管它可以帮助优化系统或流程，但人类在系统或流程的决策和控制方面仍然是不可替代的（Rezaei，2015）。人工智能融入客户绩效，将人工智能技术与客户服务相结合，提升客户绩效和满意度。借助人工智能，公司可以收集和分析客户数据，以更准确地确定客户的需求和偏好。
AI assimilation can enhance customer performance in various ways, such as through chatbots that interact directly with customers through text or voice conversations, providing them with the necessary information, addressing complaints, and offering customized solutions. In addition, AI can leverage customer data to make personalized product or service recommendations, thereby increasing customer satisfaction and loyalty. AI can also improve customer service efficiency by swiftly identifying and resolving customer issues, managing queues, and enhancing responsiveness to complaints. Overall, AI assimilation into customer performance can enhance personalization, satisfaction, and loyalty.
人工智能同化可以通过多种方式提高客户绩效，例如通过聊天机器人通过文本或语音对话直接与客户互动，为他们提供必要的信息，解决投诉并提供定制解决方案。此外，人工智能还可以利用客户数据提出个性化的产品或服务推荐，从而提高客户满意度和忠诚度。人工智能还可以通过快速识别和解决客户问题、管理队列以及增强对投诉的响应能力来提高客户服务效率。总体而言，人工智能融入客户绩效可以提高个性化、满意度和忠诚度。
A gap in the literature is the lack of comprehensive understanding of the critical factors that contribute to the impact of AI assimilation on business outcomes. Previous research has not adequately explored the relationships between AI assimilation and customer experience, customer relationship quality, and customer performance. This study aimed to address this gap in the literature by exploring these critical factors in depth.
文献中的一个空白是缺乏对人工智能同化对业务成果影响的关键因素的全面理解。先前的研究尚未充分探讨人工智能同化与客户体验、客户关系质量和客户绩效之间的关系。本研究旨在通过深入探讨这些关键因素来弥补文献中的这一空白。
The core of our investigation lies in the recognition that AI assimilation holds the key to unlocking the various dimensions of organizational success. While the existing literature hints at potential benefits such as improved customer relationships and streamlined business operations, there remains a gap in our understanding of the nuanced mechanisms through which AI assimilation exerts its influence. The theoretical contributions of this study are twofold: First, it seeks to provide a profound exploration of the theoretical perspectives on the impact of AI assimilation. By delving into the intricate connections between AI assimilation and organizational agility, customer experience, customer relationship quality, and customer performance, we aim to enrich the existing frameworks and offer a more nuanced understanding.
我们调查的核心在于认识到人工智能同化是解锁组织成功各个方面的关键。尽管现有文献暗示了改善客户关系和简化业务运营等潜在好处，但我们对人工智能同化发挥影响力的微妙机制的理解仍然存在差距。这项研究的理论贡献有两个：首先，它试图对人工智能同化的影响提供理论视角的深刻探索。通过深入研究人工智能同化与组织敏捷性、客户体验、客户关系质量和客户绩效之间的复杂联系，我们的目标是丰富现有框架并提供更细致的理解。
Second, this study bridges existing gaps in the literature by uncovering additional factors that contribute to the impact of AI assimilation on business outcomes. These factors may serve as essential variables for future research, extending our understanding of the complex interplay between AI assimilation and its consequences. From a practical standpoint, this study offers insights that extend beyond theoretical constructs. By explicitly outlining the significance of investigating the critical influence of AI assimilation, this study seeks to guide businesses in their adoption strategies. The practical implications of our findings lie in the potential for businesses to strategically leverage AI assimilation, enhance customer relationships, and optimize overall business performance.
其次，这项研究通过揭示导致人工智能同化对业务成果影响的其他因素，弥补了文献中现有的空白。这些因素可能作为未来研究的重要变量，扩展我们对人工智能同化及其后果之间复杂相互作用的理解。从实践的角度来看，这项研究提供了超越理论结构的见解。通过明确概述调查人工智能同化的关键影响的重要性，本研究旨在指导企业制定采用策略。我们的研究结果的实际意义在于企业有可能战略性地利用人工智能同化、增强客户关系并优化整体业务绩效。
In summary, this research not only strives to contribute theoretically by enhancing our understanding of the impact of AI assimilation on business outcomes but also aims to offer practical guidance to businesses navigating the evolving landscape of AI integration. Through this dual focus, we aspire to address existing literature gaps and pave the way for more informed decision-making in the realm of AI adoption.
总之，这项研究不仅致力于通过增强我们对人工智能同化对业务成果的影响的理解来做出理论上的贡献，而且旨在为企业应对不断发展的人工智能集成格局提供实践指导。通过这种双重关注，我们渴望解决现有的文献空白，并为人工智能采用领域做出更明智的决策铺平道路。
2. Literature review 2。文献综述
2.1. AI assimilation 2.1. AI同化
AI assimilation has become a crucial component in modern business and customer service landscapes. The integration of AI technology into customer service brings several benefits, such as enhancing customer experience, reducing wait times, and improving efficiency in handling issues (Bughin et al., 2018). AI can assist customers by providing accurate and relevant information, resolving simple queries, and referring to complex issues for human agents. The use of this technology in customer service also helps businesses reduce their operational costs by decreasing their reliance on human agents. This phenomenon is rapidly evolving in the current digital era, with widespread AI implementation across various sectors ranging from production automation to customer service.
人工智能同化已成为现代商业和客户服务领域的重要组成部分。将人工智能技术集成到客户服务中可以带来多种好处，例如增强客户体验、减少等待时间以及提高处理问题的效率（Bughin et al., 2018）。人工智能可以通过提供准确且相关的信息、解决简单的查询以及为人工代理处理复杂的问题来帮助客户。在客户服务中使用该技术还可以帮助企业减少对人工代理的依赖，从而降低运营成本。这种现象在当前的数字时代正在迅速发展，人工智能在从生产自动化到客户服务等各个领域得到广泛应用。
AI assimilation has brought significant benefits to human life (McIntosh et al., 2014; Sarstedt et al., 2022). First, AI enhances efficiency and productivity. AI’s ability to swiftly and accurately analyze data can assist humans in making more precise decisions (Astuti and Pratika, 2019). Furthermore, AI can be used to automate production processes, reduce human fatigue, and improve product quality (Astuti and Pratika, 2019).
人工智能同化给人类生活带来了显着的好处（McIntosh et al., 2014; Sarstedt et al., 2022）。首先，人工智能提高效率和生产力。人工智能快速、准确地分析数据的能力可以帮助人类做出更精确的决策（Astuti 和 Pratika，2019）。此外，人工智能还可用于实现生产流程自动化、减少人类疲劳并提高产品质量（Astuti 和 Pratika，2019）。
Second, AI helps humans handle tasks that are difficult or impossible to perform. AI can be utilized in disaster response contexts, such as locating victims trapped under building rubble, or in medical data analysis for disease detection in patients (Hulliyah, 2021; Liengaard et al., 2021; Rönkkö et al., 2016; Sarstedt et al., 2021). The concept of AI assimilation, the core concept of this study, is crucial because it focuses on integrating AI technology into the customer service process. The efficient integration of AI technology into customer service can enhance customer satisfaction, loyalty, and retention. Well-implemented AI technology can provide personalized and proactive services, thereby enhancing the overall customer experience.
其次，人工智能帮助人类处理难以或不可能执行的任务。人工智能可用于灾难响应环境，例如定位被困在建筑废墟下的受害者，或用于患者疾病检测的医疗数据分析（Hulliyah，2021；Liengaard 等，2021；Rönkkö 等，2016；Sarstedt 等） ., 2021)。本研究的核心概念——人工智能同化的概念至关重要，因为它侧重于将人工智能技术融入到客户服务流程中。将人工智能技术有效集成到客户服务中可以提高客户满意度、忠诚度和保留率。实施良好的人工智能技术可以提供个性化和主动的服务，从而增强整体客户体验。
However, the AI assimilation concept requires further investigation. The authors need to provide a more in-depth analysis of the meaning of this construct and the reasons behind the choice of the scales used in the methods section. This helps us understand the measurement and construct validity of the research. Additionally, the authors need to provide detailed explanations of the various dimensions of AI assimilation, such as customer perceptions, organizational readiness, and technology acceptance. AI assimilation also brings about risks such as job displacement, misuse of AI for criminal purposes, and decision-making errors. Therefore, governments and society playing a role in regulating and overseeing this technology must be wise and careful with AI to minimize its negative impact on human life.
然而，人工智能同化概念还需要进一步研究。作者需要对该结构的含义以及选择方法部分中使用的量表背后的原因进行更深入的分析。这有助于我们理解研究的测量和构建有效性。此外，作者需要对人工智能同化的各个维度进行详细解释，例如客户认知、组织准备情况和技术接受度。人工智能同化还带来工作岗位流失、滥用人工智能用于犯罪目的以及决策错误等风险。因此，在监管和监督这项技术方面发挥作用的政府和社会必须明智而谨慎地对待人工智能，以尽量减少其对人类生活的负面影响。
2.2. Organisational agility and customer agility
2.2.组织敏捷性和客户敏捷性
Organizational and customer agility are two critical components of successful businesses in the AI era. Organizational agility reflects a company’s ability to adapt quickly and respond to market changes and customer needs. Companies that encourage innovation and collaboration have flexible structures and efficient processes that can harness the potential of AI in automating tasks, improving decision-making, and enhancing customer experience (Alwreikat and Rjoub, 2020; Hanafi et al., 2021; Mayatopani, 2021; Paramita et al., 2022).
组织和客户敏捷性是人工智能时代成功企业的两个关键组成部分。组织敏捷性反映了公司快速适应和响应市场变化和客户需求的能力。鼓励创新和协作的公司拥有灵活的结构和高效的流程，可以利用人工智能在自动化任务、改进决策和增强客户体验方面的潜力（Alwreikat 和 Rjoub，2020 年；Hanafi 等人，2021 年；Mayatopani，2021 年； Paramita 等人，2022）。
On the other hand Customer agility refers to the ease and speed with which customers adopt new technologies, products, and services. AI enables customers to access information, solutions, and support in real-time and personalized manners, thereby increasing their satisfaction and convenience. Organizational and customer agility play crucial roles in helping businesses remain competitive and relevant in a fast-paced and dynamic market. With AI, organizations can enhance their agility by automating routine tasks, analyzing data, and providing tailored services to customers (Benitez et al., 2020; Richter et al., 2016; Shieh and Yeh, 2013).
另一方面，客户敏捷性是指客户采用新技术、产品和服务的难易程度和速度。人工智能使客户能够以实时和个性化的方式获取信息、解决方案和支持，从而提高他们的满意度和便利性。组织和客户敏捷性在帮助企业在快节奏、动态的市场中保持竞争力和相关性方面发挥着至关重要的作用。借助人工智能，组织可以通过自动化日常任务、分析数据以及为客户提供定制服务来增强敏捷性（Benitez 等人，2020 年；Richter 等人，2016 年；Shieh 和 Yeh，2013 年）。
2.3. Customer experience 2.3.客户体验
Customer experience is a critical factor in determining business success in the AI era. This involves creating positive and memorable interactions between a company and its customers. Companies that prioritize customer experience can enhance customer satisfaction and loyalty and gain a competitive edge in the market (AL-Khatib and Ramayah, 2023; Hayadi et al., 2023; Rosman et al., 2022). AI technology plays a crucial role in improving customer experience by providing tools for effectively managing customer interactions and personalizing services. With AI, companies can analyze customer data, identify customer preferences, and deliver experiences tailored to customer needs. The effective utilization of AI technology allows companies to boost customer satisfaction, increase loyalty, and gain a competitive advantage in the market (Chatterjee et al., 2021; da Costa et al., 2022; Hung, 2021; Zheng et al., 2011).
客户体验是人工智能时代决定业务成功的关键因素。这涉及在公司与其客户之间建立积极且令人难忘的互动。优先考虑客户体验的公司可以提高客户满意度和忠诚度，并获得市场竞争优势（AL-Khatib 和 Ramayah，2023；Hayadi 等，2023；Rosman 等，2022）。人工智能技术通过提供有效管理客户互动和个性化服务的工具，在改善客户体验方面发挥着至关重要的作用。借助人工智能，公司可以分析客户数据、识别客户偏好并提供根据客户需求量身定制的体验。人工智能技术的有效利用使企业能够提高客户满意度、忠诚度并获得市场竞争优势（Chatterjee et al., 2021；da Costa et al., 2022；Hung, 2021；Zheng et al., 2011） ）。
The theory of the customer experience in the context of AI emphasizes the importance of AI technology in enhancing customer satisfaction and loyalty. Using AI, companies can understand customer preferences and behaviors and provide personalized experiences. For example, AI-powered chatbots can quickly and efficiently respond to customer inquiries, whereas predictive analytics can help companies anticipate and meet customer needs even before they are expressed. However, companies must consider the ethical aspects of AI use, such as protecting customer privacy and preventing actions that could harm them. To achieve optimal customer satisfaction and build sustainable relationships, companies must strike a balance between leveraging AI technology and implementing ethical practices.
人工智能背景下的客户体验理论强调人工智能技术在提高客户满意度和忠诚度方面的重要性。使用人工智能，公司可以了解客户的偏好和行为并提供个性化体验。例如，人工智能驱动的聊天机器人可以快速有效地响应客户的询问，而预测分析可以帮助公司在客户表达需求之前预测并满足客户的需求。然而，公司必须考虑人工智能使用的道德方面，例如保护客户隐私并防止可能伤害他们的行为。为了实现最佳的客户满意度并建立可持续的关系，公司必须在利用人工智能技术和实施道德实践之间取得平衡。
2.4. Customer relationship quality
2.4.客户关系质量
Customer relationship quality in the context of AI is a critical factor for improving customer satisfaction in interactions with a company. By using AI, companies can provide faster and more accurate services tailored to customer needs. A concrete example is the use of AI-driven chatbots in customer relationship management, which can automatically interact with customers through text or voice messages. Chatbots can efficiently answer common customer inquiries such as product or service information, order processes, or complaints (Tseng et al., 2022).
人工智能背景下的客户关系质量是提高客户与公司互动满意度的关键因素。通过使用人工智能，企业可以根据客户需求提供更快、更准确的服务。一个具体的例子是在客户关系管理中使用人工智能驱动的聊天机器人，它可以通过文本或语音消息自动与客户交互。聊天机器人可以有效地回答常见的客户询问，例如产品或服务信息、订单流程或投诉（Tseng 等人，2022）。
Furthermore, AI can be used to analyze customer data to identify preferences and needs. Through machine learning technology, AI can recognize patterns from customer data and provide product or service recommendations that align with customer requirements. This contributes to increased customer satisfaction, as they feel that they are receiving services tailored to their needs (Fujishima, 2021). However, it’s essential to ensure that the AI used provides accurate responses to customer queries and that customer data collected and analyzed by the AI must be protected and not misused. This ensures that customers feel safe and trust their companies.
此外，人工智能可用于分析客户数据以识别偏好和需求。通过机器学习技术，人工智能可以从客户数据中识别模式，并提供符合客户需求的产品或服务推荐。这有助于提高客户满意度，因为他们觉得自己正在接受适合其需求的服务（Fujishima，2021）。然而，必须确保所使用的人工智能能够准确响应客户的查询，并且人工智能收集和分析的客户数据必须受到保护而不是被滥用。这确保客户感到安全并信任他们的公司。
2.5. Customer performance
2.5.客户表现
Customer performance in the context of AI is one of the key determinants of the success of products or services that rely on AI technology. Customer performance reflects how they respond to products and services that rely on AI. Improving customer performance through AI can enhance customer satisfaction and increase loyalty toward products or services. This is crucial for companies seeking to maintain and expand their market share in an increasingly sophisticated technological era (Chin et al., 2003; Mujali Al-Rawahna and Al Hadid, 2020; Panagou et al., 2011; Sarstedt, 2008).
人工智能背景下的客户绩效是依赖人工智能技术的产品或服务成功的关键决定因素之一。客户绩效反映了他们对依赖人工智能的产品和服务的反应。通过人工智能提高客户绩效可以提高客户满意度并提高对产品或服务的忠诚度。这对于在日益复杂的技术时代寻求维持和扩大市场份额的公司至关重要（Chin 等人，2003 年；Mujali Al-Rawahna 和 Al Hadid，2020 年；Panagou 等人，2011 年；Sarstedt，2008 年）。
One way to enhance customer performance with AI is by providing user-friendly and intuitive features. This makes it easier for customers to use products or services that rely on AI, making them feel comfortable and satisfied with their usage. In addition, companies should provide excellent technical support to customers who encounter difficulties using AI-reliant products or services. In this way, customers feel valued and appreciated, making them loyal to a product or service.
利用人工智能提高客户绩效的一种方法是提供用户友好且直观的功能。这使得客户能够更轻松地使用依赖人工智能的产品或服务，让他们对使用感到舒适和满意。此外，公司应该为在使用人工智能产品或服务时遇到困难的客户提供出色的技术支持。通过这种方式，客户会感到受到重视和赞赏，从而使他们对产品或服务忠诚。
Companies should continually improve the quality of their AI-reliant products and services through innovation and ongoing enhancements. This makes customers feel that they are continually receiving high-quality products/services, which makes them more willing to use them. Companies should improve their transparency and security when using AI-reliant products or services. This will make customers feel safe and comfortable using the product or service, build trust, and increase loyalty to the company.
企业应通过创新和持续改进，不断提高人工智能产品和服务的质量。这让客户感觉他们不断收到高质量的产品/服务，从而更愿意使用它们。公司在使用依赖人工智能的产品或服务时应提高透明度和安全性。这将使客户在使用产品或服务时感到安全和舒适，建立信任并提高对公司的忠诚度。
3. Hypothesis development
3. 假设发展
To frame our hypotheses, we draw upon the theoretical foundations established by Chatterjee et al. (2021), da Costa et al. (2022), and Zheng et al. (2011), to understand the multifaceted role of AI assimilation within organizations.
为了构建我们的假设，我们借鉴了 Chatterjee 等人建立的理论基础。 （2021），达科斯塔等人。 （2022）和郑等人。 （2011），了解人工智能同化在组织内的多方面作用。
The role of AI assimilation in organizations and customer agility connects the use of AI technology in the business world (da Costa et al., 2022). This hypothesis suggests that the implementation of AI can help companies become more agile and responsive to customer needs. Therefore, this hypothesis states that AI assimilation can help companies become more agile and responsive to customer needs. This can assist companies in improving their efficiency, reducing errors, and enhancing customer satisfaction. Furthermore, AI can help companies create products and services that align better with customer needs, thus increasing customer loyalty.
人工智能同化在组织和客户敏捷性中的作用将人工智能技术在商业世界中的使用联系起来（da Costa 等人，2022）。这一假设表明，人工智能的实施可以帮助公司变得更加敏捷并能够更快地响应客户需求。因此，这一假设指出，人工智能同化可以帮助公司变得更加敏捷并能够更快地响应客户需求。这可以帮助公司提高效率、减少错误并提高客户满意度。此外，人工智能可以帮助企业创造更符合客户需求的产品和服务，从而提高客户忠诚度。
H1
AI assimilation plays a significant role in organizational and customer agility.
人工智能同化在组织和客户敏捷性方面发挥着重要作用。
The AI assimilation hypothesis predicts that AI technology can enhance the quality of customer relationships (da Costa et al., 2022). This is because AI can help manage and analyze customer data effectively and provide quick and accurate responses tailored to customer needs. Thus, the AI assimilation hypothesis states that AI can improve the quality of customer relationships. Companies that use AI can leverage technology to manage and analyze customer data effectively and provide faster and more accurate services tailored to customer needs. This will enhance customer satisfaction and foster better relationships between the company and its customers.
人工智能同化假说预测人工智能技术可以提高客户关系的质量（da Costa et al., 2022）。这是因为人工智能可以帮助有效管理和分析客户数据，并根据客户需求提供快速、准确的响应。因此，人工智能同化假说指出人工智能可以提高客户关系的质量。使用人工智能的企业可以利用技术有效地管理和分析客户数据，并根据客户需求提供更快、更准确的服务。这将提高客户满意度并促进公司与客户之间更好的关系。
H2
AI assimilation plays a significant role in the quality of customer relationships.
人工智能同化在客户关系质量中发挥着重要作用。
The AI assimilation hypothesis states that AI technology will continue to evolve and become increasingly integrated into human life, affecting how humans experience and respond to customer experiences (Zheng et al., 2011). Additionally, AI can be used to analyze customer data and make informed decisions to enhance the customer experience. For example, AI can identify customer trends and preferences, enabling companies to offer products and services that match customer needs better. Therefore, the AI assimilation hypothesis suggests that the use of AI in customer service improves service quality and enhances customer satisfaction. However, on the flip side, there are concerns that AI may replace human roles in customer service, reducing human interaction and diminishing the personal and emotional experience for customers.
人工智能同化假说指出，人工智能技术将继续发展，并越来越多地融入人类生活，影响人类体验和响应客户体验的方式（Zheng et al., 2011）。此外，人工智能还可用于分析客户数据并做出明智的决策，以增强客户体验。例如，人工智能可以识别客户趋势和偏好，使企业能够提供更符合客户需求的产品和服务。因此，人工智能同化假说表明，在客户服务中使用人工智能可以提高服务质量并提高客户满意度。然而，另一方面，人们担心人工智能可能会取代客户服务中的人类角色，减少人际互动并减少客户的个人和情感体验。
H3
AI assimilation plays a significant role in customer experience.
人工智能同化在客户体验中发挥着重要作用。
Organizational and customer agility play crucial roles in improving the quality of customer relationships (Zheng et al., 2011). These two hypotheses are interconnected and dependent on one another to build good relationships with customers. The combination of organizational and customer agility has a positive impact on customer relationship quality. With effective organizations, companies can meet customer needs quickly and on time by providing quality products and services. With customer agility capabilities, companies can rapidly anticipate and respond to changes in customer needs, thereby enhancing customer satisfaction.
组织和客户敏捷性在提高客户关系质量方面发挥着至关重要的作用（Zheng et al., 2011）。这两种假设相互关联并相互依赖，才能与客户建立良好的关系。组织和客户敏捷性的结合对客户关系质量产生积极影响。凭借有效的组织，公司可以通过提供优质的产品和服务来快速、按时地满足客户的需求。凭借客户敏捷能力，企业可以快速预测并响应客户需求的变化，从而提高客户满意度。
H4
Organisational and customer agility play a significant role in the quality of customer relationships.
组织和客户敏捷性对于客户关系的质量起着重要作用。
The hypotheses of organizational and customer agility play a significant role in improving customer experience (Chatterjee et al., 2021). The organization serves as the structure used by companies to manage resources effectively and efficiently to achieve organizational goals. Conversely, customer agility represents the company’s ability to anticipate and respond quickly to changes in customer needs and preferences. Both organizational and customer agility can help enhance customer trust in a company or organization. If an organization can work effectively and respond quickly to customer changes, customers will feel confident that the company or organization can be trusted and provide solutions that meet their needs.
组织和客户敏捷性的假设在改善客户体验方面发挥着重要作用（Chatterjee 等人，2021）。组织是公司用来有效和高效地管理资源以实现组织目标的结构。相反，客户敏捷性代表公司预测并快速响应客户需求和偏好变化的能力。组织和客户敏捷性都有助于增强客户对公司或组织的信任。如果一个组织能够有效地工作并快速响应客户的变化，客户就会相信该公司或组织可以被信任并提供满足其需求的解决方案。
H5
Organisational and customer agility play a significant role in customer experience.
组织和客户敏捷性在客户体验中发挥着重要作用。
The hypothesis that customer experience plays a role in customer relationship quality is that a positive customer experience influences the quality of customer relationships (Chatterjee et al., 2021). In the modern era, companies must provide a good customer experience to remain competitive in challenging markets. A positive customer experience can also affect long-term customer relationships. Satisfied customers are more likely to stay connected with the company, helping them maintain long-term relationships with them. This will help the company maintain its position in the market and increase its revenue. However, negative customer experiences can also affect the quality of customer relationships. Unsatisfied customers are more likely to discontinue their relationship with the company or complain to others. This can lead to customer loss and damage the company’s reputation in the eyes of other customers.
客户体验在客户关系质量中发挥作用的假设是，积极的客户体验会影响客户关系的质量（Chatterjee 等人，2021）。在当今时代，公司必须提供良好的客户体验，才能在充满挑战的市场中保持竞争力。积极的客户体验也会影响长期的客户关系。满意的客户更有可能与公司保持联系，帮助他们维持长期关系。这将有助于公司保持其市场地位并增加收入。然而，负面的客户体验也会影响客户关系的质量。不满意的客户更有可能终止与公司的关系或向他人投诉。这可能会导致客户流失并损害公司在其他客户眼中的声誉。
H6
Customer experience plays a significant role in the quality of customer relationships.
客户体验对客户关系的质量起着重要作用。
The hypothesis that customer relationship quality plays a crucial role in customer performance states that good relationships between customers and companies can create trust and satisfaction, making customers more likely to continue interacting with and engaging with the company’s services (Panagou et al., 2011). Good customer relationship quality can influence the level of AI expertise in providing services that satisfy customer needs and desires. AI, with accurate information about customer preferences and needs, is better equipped to provide services that meet customer expectations, resulting in greater customer satisfaction and continued interaction. In addition, the quality of customer relationships can affect an AI’s ability to identify and address customer issues. AI with accurate information on customer complaints and problems will be better equipped to provide solutions that meet customer needs, resulting in greater customer satisfaction and continued use of the company’s services.
客户关系质量在客户绩效中起着至关重要的作用的假设表明，客户和公司之间的良好关系可以建立信任和满意度，使客户更有可能继续与公司的服务互动和参与（Panagou 等，2011）。良好的客户关系质量可以影响人工智能在提供满足客户需求和愿望的服务方面的专业水平。人工智能凭借有关客户偏好和需求的准确信息，能够更好地提供满足客户期望的服务，从而提高客户满意度和持续互动。此外，客户关系的质量会影响人工智能识别和解决客户问题的能力。拥有有关客户投诉和问题的准确信息的人工智能将能够更好地提供满足客户需求的解决方案，从而提高客户满意度并持续使用公司的服务。
H7
Customer relationship quality plays a significant role in customer performance.
客户关系质量对客户绩效起着重要作用。
By anchoring our hypotheses to established theoretical frameworks, we aim to provide a robust foundation for empirical investigation, offering valuable insights into the intricate dynamics of AI assimilation within the organizational and customer contexts. Fig. 1 illustrates the proposed research framework.
通过将我们的假设锚定在既定的理论框架上，我们的目标是为实证研究提供坚实的基础，为组织和客户环境中人工智能同化的复杂动态提供有价值的见解。图 1 说明了所提出的研究框架。
- Research framework.
图 1 研究框架。
4. Methodology 4. 方法论
To distribute the self-assessment survey, we used the online platform ‘Forms’ to reach potential respondents from the target population of experienced AI users and developers in Indonesia; the survey period spanned from August to December 2022. A rigorous screening process was employed to ensure the validity of the participant responses. The criterion for selecting respondents included their practical experience of using or implementing AI in their professional or personal activities.
为了分发自我评估调查，我们使用在线平台“Forms”来接触印度尼西亚经验丰富的人工智能用户和开发人员目标人群中的潜在受访者；调查期间为2022年8月至12月。我们采用了严格的筛选流程来确保参与者回答的有效性。选择受访者的标准包括他们在专业或个人活动中使用或实施人工智能的实际经验。
The screening process involved asking participants about their level of practical experience with AI, the nature of their work or personal involvement with AI technologies, and any specific projects or applications in which they had been engaged. Respondents who did not meet the criteria for practical experience with AI were excluded. Of the initial 392 participants, 382 qualified respondents successfully met the screening criteria, ensuring that the sample truly represented the population of experienced AI users and developers in Indonesia.
筛选过程涉及询问参与者的人工智能实践经验水平、他们的工作性质或个人参与人工智能技术的情况，以及他们参与的任何具体项目或应用。不符合人工智能实践经验标准的受访者被排除在外。在最初的 392 名参与者中，382 名合格的受访者成功满足了筛选标准，确保样本真正代表了印度尼西亚经验丰富的人工智能用户和开发人员群体。
The decision to include both AI users and developers in this study was driven by the intention to capture a comprehensive perspective on AI assimilation. Users provide valuable insights into the practical applications and impacts of AI, whereas developers contribute to a technical understanding of the implementation process. Organizational size, industry sector, and years of experience were incorporated into the research model to control for potential confounding factors. These variables enhance the robustness of our study by accounting for the demographic and contextual variations that may influence the relationship between AI assimilation and business outcomes. According to Krejcie and Morgan (1970), a sample size of 384 is appropriate for a population of 100,000. The respondents’ demographic characteristics are listed in Table 1.
将人工智能用户和开发人员纳入本研究的决定是为了获得人工智能同化的全面视角。用户提供有关人工智能实际应用和影响的宝贵见解，而开发人员则有助于对实施过程的技术理解。研究模型中纳入了组织规模、行业领域和多年经验，以控制潜在的混杂因素。这些变量通过考虑可能影响人工智能同化与业务成果之间关系的人口统计和背景变化，增强了我们研究的稳健性。根据 Krejcie 和 Morgan (1970) 的说法，384 个样本量适合 100,000 人的人口。受访者的人口统计特征列于表1。
Table 1. Respondent demographics.
表 1. 受访者人口统计数据。
| Characteristics 特征 | Item 物品 | Frequency 频率 | Percentage 百分比 |
|---|---|---|---|
| Gender 性别 | Male 男性 | 201 | 52.6% |
| Female 女性 | 181 | 47.4% | |
| Age | 16–20 | 89 | 23.2% |
| 21–25 | 177 | 46.3% | |
| >26 | 116 | 30.3% | |
| Education Level 教育程度 | High School 中学 | 66 | 17.2% |
| Undergraduate 大学本科 | 196 | 51.3% | |
| Graduate 毕业 | 120 | 31.5% | |
| Experiences Using AI Applications 使用人工智能应用程序的经验 |
>1 Year >1年 | 201 | 52.6% |
| <1 Year <1年 | 181 | 47.4% |
This survey was divided into two parts: one to collect demographic information and the other to test the hypotheses. The questions were based on previous research and validated scales. The validity of the questionnaire was confirmed. The 7-Likert scales were used to enhance the measurement accuracy.
这项调查分为两部分：一是收集人口统计信息，二是检验假设。这些问题是基于之前的研究和经过验证的量表。确认了问卷的有效性。 7-Likert量表用于提高测量精度。
VIF is the Variance Inflation Factor. It is a statistical measure used to assess the severity of multicollinearity in a regression analysis. Multicollinearity occurs when there is a high correlation between the independent variables in a regression model, which can result in unreliable and inaccurate coefficient estimates and standard errors. VIF analysis evaluates the level of multicollinearity among the predictor variables in a regression model by calculating the variance ratio of each coefficient of an independent variable to the variance of a coefficient that would be obtained if that variable was uncorrelated with the other predictors. The use of Variance Inflation Factor (VIF) to examine common method bias is unconventional, as VIF is traditionally employed to assess multicollinearity in regression analysis, not to specifically address issues related to the systematic variance introduced by the data collection method (Wahyuningsih, 2021). In this study, multicollinearity among the constructs was assessed using VIF analysis (Table 2). The results indicated that the inner VIF values ranged from 1.000 to 4.649, indicating that there was no multicollinearity among the latent constructs. This is consistent with the recommendation of Hair et al. (2017) that VIF values below 5.0 are required to maintain model relevance.
VIF 是方差膨胀因子。它是一种统计指标，用于评估回归分析中多重共线性的严重程度。当回归模型中的自变量之间存在高度相关性时，就会出现多重共线性，这可能会导致不可靠且不准确的系数估计和标准误差。 VIF 分析通过计算自变量的每个系数与系数方差的方差比来评估回归模型中预测变量之间的多重共线性水平，如果该变量与其他预测变量不相关，则将获得该系数的方差。使用方差膨胀因子 (VIF) 来检查共同方法偏差是非常规的，因为 VIF 传统上用于评估回归分析中的多重共线性，而不是专门解决与数据收集方法引入的系统方差相关的问题（Wahyuningsih，2021） 。在本研究中，使用 VIF 分析评估了构建体之间的多重共线性（表 2）。结果表明，内部 VIF 值范围为 1.000 至 4.649，表明潜在构建体之间不存在多重共线性。这与 Hair 等人的建议一致。 (2017) 需要 VIF 值低于 5.0 才能保持模型相关性。
Table 2. Inner VIF results.
表 2. 内部 VIF 结果。
| Construct 构造 | VIF |
|---|---|
| AIS → OCA | 1.000 |
| AIS → CRQ | 4.649 |
| AIS → CE | 3.899 |
| OCA → CRQ | 4.455 |
| OCA → CE | 3.899 |
| CE → CRQ CE→CRQ | 4.170 |
Notes: AIS: AI Assimilation; OCA: Customer Organization and Agility; CE: Customer Experience; CRQ: Customer Relationship Quality; CP: Customer Performance.
注：AIS：人工智能同化； OCA：客户组织和敏捷性； CE：客户体验； CRQ：客户关系质量； CP：客户绩效。
5. Data analysis 5. 数据分析
This study employed the SmartPLS 3 software for measurement and partial least squares (PLS) analysis. Table 3 presents the measurement variables used in this study. Reliability and validity tests were conducted during the measurement stage, and the structural model and path coefficients were evaluated during the analysis stage. The objectives of both stages were to confirm the reliability and validity of the constructs and test their relationships. This study focuses on the cause-and-effect relationships between SIA practices, OCA, CE, CRQ, and CP, all of which consist of multiple previously studied measurement points.
本研究采用 SmartPLS 3 软件进行测量和偏最小二乘 (PLS) 分析。表 3 列出了本研究中使用的测量变量。测量阶段进行信度和效度测试，分析阶段评估结构模型和路径系数。这两个阶段的目标都是确认结构的可靠性和有效性并测试它们的关系。本研究重点关注 SIA 实践、OCA、CE、CRQ 和 CP 之间的因果关系，所有这些都由多个先前研究的测量点组成。
Table 3. Questionnaire measurement items.
表3. 问卷测量项目。
| Measured Items 测量项目 | |
|---|---|
| AI Assimilation, source: (Alshahrani et al., 2022; Maddy and Boukabara, 2021; Prikshat et al., 2023; Wamba, 2022) AI 同化，来源：（Alshahrani 等人，2022 年；Maddy 和 Boukabara，2021 年；Prikshat 等人，2023 年；Wamba，2022 年） |
|
| AIS1 自动识别系统1 | AI helps me organise my day. 人工智能帮助我安排我的一天。 |
| AIS2 自动识别系统2 | AI helps me find information or answers to my questions. 人工智能帮助我找到信息或问题的答案。 |
| AIS3 自动识别系统3 | AI helps me anticipate my needs or preferences by recommending appropriate products or services. 人工智能通过推荐合适的产品或服务来帮助我预测我的需求或偏好。 |
| AIS4 自动识别系统4 | AI helps me reduce the time it takes to complete routine tasks. 人工智能帮助我减少完成日常任务所需的时间。 |
| AIS5 自动识别系统5 | AI makes it easier for me to access and manage information and documents. 人工智能使我可以更轻松地访问和管理信息和文档。 |
| Organization and Customer Agility, source: (Crocitto and Youssef, 2003; Kalaignanam et al., 2021; Wamba, 2022) 组织和客户敏捷性，来源：（Crocitto 和 Youssef，2003 年；Kalaignnam 等人，2021 年；Wamba，2022 年） |
|
| OCA1 光学CA1 | I feel that by using AI in its business processes, the company I work for is very responsive to customer needs. 我觉得通过在业务流程中使用人工智能，我工作的公司对客户需求的响应非常迅速。 |
| OCA2 光学CA2 | AI has helped my company improve the speed and efficiency of responding to customer requests. 人工智能帮助我的公司提高了响应客户请求的速度和效率。 |
| OCA3 光学CA3 | After using AI in business processes, I feel that customers are more satisfied with my company’s services. 在业务流程中使用人工智能后，我觉得客户对我公司的服务更加满意。 |
| OCA4 | AI has helped my company anticipate customer needs and provide more appropriate solutions. AI帮助我的公司预测客户需求并提供更合适的解决方案。 |
| OCA5 奥卡5 | I feel my company is more customer-centric after using AI to improve agility in responding to customer needs. 我觉得在使用人工智能提高响应客户需求的敏捷性后，我的公司更加以客户为中心。 |
| Customer Experience, source: (Wamba, 2022) 客户体验，来源：（Wamba，2022） |
|
| CE1 | AI has helped my company process data quickly and accurately, speeding the decision-making process. 人工智能帮助我的公司快速准确地处理数据，加快了决策过程。 |
| CE2 | AI has helped my company improve operational efficiency by eliminating time-consuming, repetitive tasks. 人工智能消除了耗时的重复性任务，帮助我的公司提高了运营效率。 |
| CE3 | My company’s use of AI has improved customer satisfaction by providing faster and more targeted services. 我公司对人工智能的使用通过提供更快、更有针对性的服务提高了客户满意度。 |
| CE4 | AI has helped my company improve the personalisation of products and services so that customers have a sense of individual value and recognition. AI帮助我公司提高了产品和服务的个性化程度，让客户有个体价值感和认可感。 |
| CE5 | Despite some barriers to implementing AI in my company, I believe the benefits far outweigh the risks. 尽管在我的公司实施人工智能存在一些障碍，但我相信好处远远大于风险。 |
| Customer Relationship Quality, source: (Mujahid Ghouri et al., 2021; Rakhmansyah et al., 2022; Wei et al., 2010) 客户关系质量，来源：（Mujahid Ghouri 等人，2021 年；Rakhmansyah 等人，2022 年；Wei 等人，2010 年） |
|
| CRQ1 | AI can help improve customer relationships quality by providing faster and more accurate customer service. 人工智能可以通过提供更快、更准确的客户服务来帮助提高客户关系质量。 |
| CRQ2 | The use of AI can help anticipate customer needs and provide product or service recommendations that meet those needs. 人工智能的使用可以帮助预测客户需求并提供满足这些需求的产品或服务建议。 |
| CRQ3 | AI can help improve the customer experience by providing a more personalised and targeted shopping experience. 人工智能可以通过提供更加个性化和有针对性的购物体验来帮助改善客户体验。 |
| CRQ4 | The use of AI can help manage and store customer data more efficiently, making it easier to provide better service to customers. 人工智能的使用可以帮助更高效地管理和存储客户数据，从而更轻松地为客户提供更好的服务。 |
| CRQ5 | AI can help build customer trust by providing faster and more accurate service and minimising errors when handling customer complaints. 人工智能可以通过提供更快、更准确的服务并最大限度地减少处理客户投诉时的错误来帮助建立客户信任。 |
| Customer Performance, source: (Baabdullah et al., 2019; Wamba, 2022) 客户绩效，来源：（Baabdullah 等人，2019 年；Wamba，2022 年） |
|
| CP1 | Customer performance with AI in business depends on their trust and understanding of the technology. 客户在业务中使用人工智能的表现取决于他们对技术的信任和理解。 |
| CP2 | Customers with a better understanding of AI are more likely to trust it and use it in their business processes. 对人工智能有更好了解的客户更有可能信任它并在其业务流程中使用它。 |
| CP3 | Customers who are uncomfortable with AI or do not understand how it works tend to avoid using AI in their business. 对人工智能感到不舒服或不了解其工作原理的客户往往会避免在其业务中使用人工智能。 |
| CP4 | Effective use of AI can improve customer performance and create greater value for their business. 有效利用人工智能可以提高客户绩效并为其业务创造更大价值。 |
| CP5 | Customers who feel involved and informed about the use of AI in their business tend to be more trusting and open to its use. 那些感觉自己参与并了解人工智能在其业务中的使用的客户往往会更加信任并对其使用持开放态度。 |
Note: AIS: AI Assimilation; OCA: Organization and Customer Agility; CE: Customer Experience; CRQ: Customer Relationship Quality; CP: Customer Performance.
注：AIS：AI同化； OCA：组织和客户敏捷性； CE：客户体验； CRQ：客户关系质量； CP：客户绩效。
The research conducted by Alshahrani et al. (2022) explains the crucial role of AI in transforming the public sector and its growth over the past few decades. AI has significant potential to transform the development and delivery of public-sector services, with an estimated annual productivity increase of 2% over the next 15 years. This can be achieved through more efficient resource allocation, automation of repetitive tasks, reduction of dependence on human decision-making, and addressing the limitations of previous e-government initiatives. The use of AI has also been extended to sectors beyond the public sector, becoming a crucial element in organizational competitive advantage strategies across various industries. Predictions indicate that global spending on AI will reach nearly $98 billion by 2023, which is more than double the amount spent in 2019. Approximately 24% of the global GDP is expected to come from AI technology by 2025. Despite enthusiasm for and investment in AI adoption in the public sector, the AI assimilation process within public-sector organizations appears fragmented and lags behind the private sector. This study aims to identify challenges related to the AI assimilation process using the Attention-Based View (ABV) organizational framework as a theoretical lens. This study is expected to provide valuable insights into the changes that occur during the AI assimilation process and to support public sector organizations in their AI adoption initiatives, particularly in Saudi Arabia.
Alshahrani 等人进行的研究。 (2022) 解释了人工智能在过去几十年公共部门转型及其增长中的关键作用。人工智能在改变公共部门服务的开发和提供方面具有巨大潜力，预计未来 15 年生产力每年将增长 2%。这可以通过更有效的资源分配、重复性任务的自动化、减少对人类决策的依赖以及解决以前电子政务举措的局限性来实现。人工智能的使用也已扩展到公共部门以外的领域，成为各行业组织竞争优势战略的关键要素。预测显示，到 2023 年，全球人工智能支出将达到近 980 亿美元，是 2019 年支出的两倍多。到 2025 年，全球 GDP 的约 24% 预计将来自人工智能技术。尽管人们对人工智能充满热情和投资尽管在公共部门采用人工智能，但公共部门组织内的人工智能同化过程显得分散且落后于私营部门。本研究旨在使用基于注意力的视图（ABV）组织框架作为理论镜头，识别与人工智能同化过程相关的挑战。这项研究预计将为人工智能同化过程中发生的变化提供有价值的见解，并支持公共部门组织的人工智能采用计划，特别是在沙特阿拉伯。
The research conducted by Maddy and Boukabara (2021) discusses the development of a multi-instrument pre-processing system for remote sensing called the Multi-Instrument Inversion and Data Assimilation Pre-processing System-Artificial Intelligence (MIIDAPS-AI). MIIDAPS-AI utilizes modern AI techniques in the field of remote sensing to streamline traditional remote sensing algorithms such as the Microwave Integrated Retrieval System (MiRS) for microwave sensors and the NOAA Unique CrIS/ATMS Processing System (NUCAPS) for infrared sensors. This is essential because of the rapidly growing volume of data for various reasons, including the increasing number of small Earth observation satellites (smallsats and CubeSats), the deployment of more satellites by international partners, and the enhanced capabilities of new sensors for higher spatial, temporal, and spectral resolution environmental measurements. Neural networks have been used in remote sensing for several decades. Neural network algorithms, as well as new AI and machine learning (ML) techniques, offer a method to approximate any function, whether linear or nonlinear, without assuming input error characteristics. This research leverages the remarkable advancements in AI and ML, including the increasing availability of AI computing nodes, user-friendly high-level programming languages for developing AI/ML algorithms, and the ability to develop advanced deep-learning algorithms. MIIDAPS-AI is a next-generation remote-sensing algorithm based on modern AI and ML techniques that can be used to derive various geophysical products from multiple satellite sensors.
Maddy 和 Boukabara（2021）进行的研究讨论了一种用于遥感的多仪器预处理系统的开发，称为多仪器反演和数据同化预处理系统-人工智能（MIIDAPS-AI）。 MIIDAPS-AI利用遥感领域的现代人工智能技术来简化传统的遥感算法，例如用于微波传感器的微波集成检索系统（MiRS）和用于红外传感器的NOAA独特的CrIS/ATMS处理系统（NUCAPS）。这是至关重要的，因为由于各种原因，数据量迅速增长，包括小型地球观测卫星（小型卫星和立方体卫星）数量的增加、国际合作伙伴部署更多卫星，以及新传感器的更高空间、更广泛的能力的增强。时间和光谱分辨率环境测量。神经网络在遥感领域的应用已有数十年历史。神经网络算法以及新的人工智能和机器学习 (ML) 技术提供了一种逼近任何函数（无论是线性还是非线性）的方法，而无需假设输入误差特征。这项研究利用了人工智能和机器学习领域的显着进步，包括人工智能计算节点可用性的不断提高、用于开发人工智能/机器学习算法的用户友好的高级编程语言，以及开发先进深度学习算法的能力。 MIIDAPS-AI是基于现代人工智能和机器学习技术的下一代遥感算法，可用于从多个卫星传感器获取各种地球物理产品。
Furthermore, the MIIDAPS-AI can be used as pre-processing for data assimilation and data fusion. This article describes the theoretical foundation and implementation of MIIDAPS-AI, the products produced by MIIDAPS-AI, and the statistical evaluation of MIIDAPS-AI products compared to weather forecast models, radiosondes, and traditional remote sensing algorithms. This article also discusses the interpretability and confidence aspects of the MIIDAPS-AI results.
此外，MIIDAPS-AI还可用作数据同化和数据融合的预处理。本文介绍了MIIDAPS-AI的理论基础和实现、MIIDAPS-AI产生的产品，以及MIIDAPS-AI产品与天气预报模型、无线电探空仪和传统遥感算法相比的统计评估。本文还讨论了 MIIDAPS-AI 结果的可解释性和置信度方面。
The research conducted by Prikshat et al. (2023) aims to bridge the gap in understanding the assimilation of AI in human resource management (HRM) by introducing the concept of HRM(AI), which describes the use of AI in HRM and develops an assimilation framework for HRM(AI) based on the four stages of initiation, adoption, routinization, and infusion, as per innovation assimilation theory. This study identifies the factors and consequences of HRM(AI) assimilation that significantly contribute to the understanding of how AI affects various aspects of human resource management, such as recruitment, training, performance management, talent management, employee turnover, compensation management, job design, employee satisfaction, and employee engagement. Thus, this study provides a significant theoretical and practical foundation for understanding and developing AI in an HRM context.
Prikshat 等人进行的研究。 （2023）旨在通过引入HRM（AI）的概念来弥合理解人工智能在人力资源管理（HRM）中的同化的差距，该概念描述了人工智能在人力资源管理中的使用，并开发了基于人力资源管理（AI）的同化框架根据创新同化理论，分为启动、采用、常规化和注入四个阶段。本研究确定了人力资源管理（人工智能）同化的因素和后果，这些因素和后果极大地有助于理解人工智能如何影响人力资源管理的各个方面，例如招聘、培训、绩效管理、人才管理、员工流动率、薪酬管理、工作设计、员工满意度和员工敬业度。因此，这项研究为在人力资源管理背景下理解和开发人工智能提供了重要的理论和实践基础。
The research conducted by Wamba (2022) focused on the impact of AI assimilation on company performance while considering the factors that influence organizational agility. Agility refers to the ability to thrive and succeed in a dynamic and competitive business environment, with rapid and effective responses to market changes. The factors studied include system quality, Business Intelligence (BI) adoption, and IT infrastructure flexibility. System quality refers to the desirable characteristics of information systems, such as ease of use, system features, response time, and flexibility. BI assists management by providing predictions, patterns, and decision support. A flexible IT infrastructure enables the integration and reconfiguration of existing resources to develop new capabilities. This study also observes competitive performance as a mediator between these factors and organizational agility. The results are expected to provide insights into how AI assimilation can impact company performance through increased organizational agility, especially in the context of online fashion retailing.
Wamba (2022) 进行的研究重点关注人工智能同化对公司绩效的影响，同时考虑影响组织敏捷性的因素。敏捷性是指在动态和竞争的商业环境中蓬勃发展并取得成功的能力，能够快速有效地响应市场变化。研究的因素包括系统质量、商业智能 (BI) 采用和 IT 基础设施灵活性。系统质量是指信息系统所期望的特性，例如易用性、系统特性、响应时间和灵活性。 BI 通过提供预测、模式和决策支持来协助管理。灵活的 IT 基础设施可以集成和重新配置现有资源以开发新功能。这项研究还观察了竞争绩效作为这些因素和组织敏捷性之间的中介因素。研究结果预计将有助于深入了解人工智能同化如何通过提高组织敏捷性来影响公司绩效，特别是在在线时尚零售的背景下。
In this study, PLS was considered a more appropriate choice than the other SEM analysis methods for several reasons. First, PLS is ideal for investigating the causal relationships among variables while simultaneously managing the modeling and measurement variables. In addition, PLS can be used to evaluate complex predictive models with numerous constructs and variables. In this study, various paths and relationships among SIA, OCA, CE, CRQ, and CP, which are considered complex models, were examined. To conduct a PLS analysis, the sample size should be five to ten times larger than the number of paths in the model. In this study, the sample size was 382, and the number of paths was seven, meeting the requirements for PLS analysis. Furthermore, PLS is superior to covariance-based SEM as it can handle both reflective and formative indicator models, whereas other analysis methods can only evaluate reflective indicators.
在本研究中，出于多种原因，PLS 被认为是比其他 SEM 分析方法更合适的选择。首先，PLS 非常适合研究变量之间的因果关系，同时管理建模和测量变量。此外，PLS 可用于评估具有众多构造和变量的复杂预测模型。在本研究中，研究了 SIA、OCA、CE、CRQ 和 CP 之间被认为是复杂模型的各种路径和关系。要进行 PLS 分析，样本大小应比模型中的路径数大五到十倍。本研究样本量为382，路径数为7，满足PLS分析的要求。此外，PLS 优于基于协方差的 SEM，因为它可以处理反射指标模型和形成指标模型，而其他分析方法只能评估反射指标。
However, the PLS method has several limitations. First, the model parameters are optimized in two stages, which can lead to bias and errors when estimating the structural path coefficients. To avoid this issue, AI experts rigorously analyzed the questionnaire to ensure that the indicators were adequate. Additionally, there is no single global measure of model fit in PLS-SEM, making testing and validation challenging. Therefore, it is important to evaluate the data thoroughly and consider alternative approaches for confirmatory analysis.
然而，PLS 方法有一些局限性。首先，模型参数分两个阶段进行优化，这可能会导致估计结构路径系数时出现偏差和错误。为了避免这个问题，人工智能专家对问卷进行了严格的分析，以确保指标充足。此外，PLS-SEM 中没有单一的全局模型拟合度量，这使得测试和验证具有挑战性。因此，彻底评估数据并考虑验证性分析的替代方法非常重要。
5.1. External model and validation
5.1.外部模型和验证
Three main factors were used to assess the external model: reliability, concurrent validity, and discriminant validity. All components of the model were reliable, with composite reliability values greater than 0.7, which are considered excellent predictors of construct reliability. Concerning concurrent validity, the predictor variable loadings were greater than 0.5, and the average variance extracted (AVE) was greater than 0.5, which is consistent with Fornell and Larcker’s recommendations. The discriminant validity of the model was also good, as shown in Table 4, Table 5, as the factor loadings for each construct were greater than those for the other constructs.
使用三个主要因素来评估外部模型：可靠性、同时效度和判别效度。该模型的所有组件都是可靠的，综合可靠性值大于 0.7，这被认为是结构可靠性的出色预测指标。关于并发有效性，预测变量载荷大于 0.5，提取的平均方差 (AVE) 大于 0.5，这与 Fornell 和 Larcker 的建议一致。该模型的判别效度也很好，如表 4、表 5 所示，因为每个构造的因子载荷大于其他构造的因子载荷。
Table 4. Reliability analysis and convergent validity.
表 4. 可靠性分析和收敛效度。
| Measurement Construct 测量构造 | Measurement Items 测量项目 | Factor Loading 因子载荷 | Cronbach Alpha 克朗巴赫阿尔法 | Composite Reliability 综合可靠性 | AVE |
|---|---|---|---|---|---|
| AI Assimilation 人工智能同化 | AIS1 自动识别系统1 | 0.825 | 0.872 | 0.907 | 0.661 |
| AIS2 自动识别系统2 | 0.782 | ||||
| AIS3 自动识别系统3 | 0.809 | ||||
| AIS4 自动识别系统4 | 0.827 | ||||
| AIS5 自动识别系统5 | 0.822 | ||||
| Organisation and Customer Agility 组织和客户敏捷性 |
OCA1 光学CA1 | 0.845 | 0.876 | 0.910 | 0.669 |
| OCA2 光学CA2 | 0.822 | ||||
| OCA3 光学CA3 | 0.808 | ||||
| OCA4 | 0.810 | ||||
| OCA5 奥卡5 | 0.804 | ||||
| Customer Experience 客户体验 | CE1 | 0.802 | 0.861 | 0.900 | 0.642 |
| CE2 | 0.793 | ||||
| CE3 | 0.810 | ||||
| CE4 | 0.776 | ||||
| CE5 | 0.827 | ||||
| Customer Relationship Quality 客户关系质量 |
CRQ1 | 0.819 | 0.882 | 0.914 | 0.679 |
| CRQ2 | 0.816 | ||||
| CRQ3 | 0.827 | ||||
| CRQ4 | 0.834 | ||||
| CRQ5 | 0.824 | ||||
| Customer Performance 客户表现 | CP1 | 0.819 | 0.868 | 0.905 | 0.655 |
| CP2 | 0.835 | ||||
| CP3 | 0.805 | ||||
| CP4 | 0.806 | ||||
| CP5 | 0.781 |
Notes: AIS: AI Assimilation; OCA: Customer Organization and Agility; CE: Customer Experience; CRQ: Customer Relationship Quality; CP: Customer Performance.
注：AIS：人工智能同化； OCA：客户组织和敏捷性； CE：客户体验； CRQ：客户关系质量； CP：客户绩效。
Table 5. Discriminant validity.
表 5. 区分效度。
| Empty Cell | AIS | CE | CP | CRQ | OCA |
|---|---|---|---|---|---|
| AIS | 0.888 | ||||
| CE | 0.854 | 0.873 | |||
| CP | 0.863 | 0.867 | 0.871 | ||
| CRQ | 0.860 | 0.855 | 0.853 | 0.861 | |
| OCA | 0.842 | 0.857 | 0.845 | 0.838 | 0.859 |
Notes: AIS: AI Assimilation; OCA: Customer Organization and Agility; CE: Customer Experience; CRQ: Customer Relationship Quality; CP: Customer Performance.
注：AIS：人工智能同化； OCA：客户组织和敏捷性； CE：客户体验； CRQ：客户关系质量； CP：客户绩效。
5.2. Inner model results and hypothesis testing
5.2.内模型结果和假设检验
The proposed hypotheses were rigorously tested using a partial least squares (PLS) model (Chi, 2021; Endsuy, 2021; Wahyuningsih, 2021). The results presented in Table 6 are convincing and provide strong evidence for the validity of the proposed hypotheses. The data presented in the table not only shows the statistical significance of the relationship between variables but also the magnitude of their impact through path coefficients, p-values, and t-values. In conclusion, the results of the PLS analysis offer a clear confirmation of the research hypotheses and provide valuable insights into the internal workings of the model.
所提出的假设使用偏最小二乘 (PLS) 模型进行了严格测试（Chi，2021；Endsuy，2021；Wahyuningsih，2021）。表 6 中的结果令人信服，并为所提出假设的有效性提供了有力的证据。表中提供的数据不仅显示了变量之间关系的统计显着性，还通过路径系数、p 值和 t 值显示了它们的影响大小。总之，PLS 分析的结果清楚地证实了研究假设，并为模型的内部运作提供了有价值的见解。
Table 6. Summary of inner model results.
表 6. 内部模型结果汇总。
| Hypothesis 假设 | Path Coefficient 路径系数 | T-Value T值 | Results 结果 | |
|---|---|---|---|---|
| H1 | AIS → OCA | 0.514 | 7.024 | Accepted 公认 |
| H2 | AIS → CRQ | 0.155 | 2.133 | Accepted 公认 |
| H3 | AIS → CE | 0.402 | 6.985 | Accepted 公认 |
| H4 | OCA → CRQ | 0.155 | 6.435 | Accepted 公认 |
| H5 | OCA → CE | 0.514 | 9.313 | Accepted 公认 |
| H6 | CE → CRQ CE→CRQ | 0.397 | 4.838 | Accepted 公认 |
| H7 | AIS → OCA | 0.514 | 5.470 | Accepted 公认 |
Notes: AIS: AI Assimilation; OCA: Customer Organization and Agility; CE: Customer Experience; CRQ: Customer Relationship Quality; CP: Customer Performance.
注：AIS：人工智能同化； OCA：客户组织和敏捷性； CE：客户体验； CRQ：客户关系质量； CP：客户绩效。
The results presented in Table 7 and Fig. 2 provide substantial support for the proposed hypotheses. AI assimilation was found to have a significant and positive impact on customer organisation and agility (H1: AIS → OCA, β = 0.291, t-value = 6.601), as well as customer relationship quality (H2: AIS → CRQ, β = 0.862, t-value = 4.536) and customer experience (H3: AIS → CE, β = 0.381, t-value = 18.707). Furthermore, this study confirms that customer organisation and agility also contribute to the improvement of customer relationship quality (H4: OCA → CRQ, β = 0.269, t-value = 3.117) and customer experience (H5: OCA → CE, β = 0.549, t-value = 9.021), while customer experience has a significant and positive impact on customer relationship quality (H6: CE → CRQ, β = 0.399, t-value = 5.524). Finally, the results clearly show that customer relationship quality has a positive impact on customer performance (H7: CRQ → CP, β = 0.872, t-value = 18.222). The results of this study provide strong evidence for the positive impact of AI assimilation on various organizational and customer-related outcomes.
表 7 和图 2 中的结果为所提出的假设提供了实质性支持。研究发现人工智能同化对客户组织和敏捷性（H1：AIS → OCA，β = 0.291，t 值 = 6.601）以及客户关系质量（H2：AIS → CRQ，β = 0.862）产生显着且积极的影响，t 值 = 4.536）和客户体验（H3：AIS → CE，β = 0.381，t 值 = 18.707）。此外，本研究证实客户组织和敏捷性也有助于改善客户关系质量（H4：OCA → CRQ，β = 0.269，t 值 = 3.117）和客户体验（H5：OCA → CE，β = 0.549， t值= 9.021），而客户体验对客户关系质量具有显着且积极的影响（H6：CE→CRQ，β= 0.399，t值= 5.524）。最后，结果清楚地表明客户关系质量对客户绩效具有积极影响（H7：CRQ → CP，β = 0.872，t 值 = 18.222）。这项研究的结果为人工智能同化对各种组织和客户相关结果的积极影响提供了有力的证据。
Table 7. Mediation test results.
表 7. 中介测试结果。
| Construct 构造 | Construct Relationship 构建关系 | T-Value of Path Coefficient 路径系数的T值 |
Sobel Test 索贝尔测试 |
|---|---|---|---|
| AIS → OCA → CRQ | AIS → OCA | 18.707 | 0.226 |
| OCA → CRQ | 3.117 | ||
| OCA → CE → CRQ | OCA → CE | 9.021 | 0.215 |
| CE → CRQ CE→CRQ | 5.524 | ||
| AIS → CE → CRQ | AIS → CE | 6.601 | 0.160 |
| CE → CRQ CE→CRQ | 5.524 | ||
| OCA → CRQ → CP | OCA → CRQ | 3.117 | 0.150 |
| CRQ → CP | 18.222 | ||
| CE → CRQ → CP CE→CRQ→CP |
CE → CRQ CE→CRQ | 5.524 | 0.340 |
| CRQ → CP | 18.222 | ||
| AIS → CRQ → CP | AIS → CRQ | 4.536 | 0.249 |
| CRQ → CP | 18.222 | ||
| AIS → OCA → CE | AIS → OCA | 18.707 | 0.460 |
| OCA → CE | 9.021 |
Fig. 2.
2.Inner model results framework.
图 2. 内部模型结果框架。
5.3. Testing for mediating effects
5.3.中介效应测试
In this study, the role of the mediating variables was rigorously assessed using path analysis and Sobel’s test. The results of the Sobel test, as shown in Table 7, provide evidence of the significance of the mediators by calculating the Z and p values. The results showed that all Z-values of the mediators exceeded the threshold of 0.01, indicating a significant mediating effect between the independent and dependent variables. In other words, the mediating variables influence the relationship between the independent and dependent variables and should be considered in future research on this topic.
在本研究中，使用路径分析和索贝尔检验严格评估中介变量的作用。 Sobel 检验的结果（如表 7 所示）通过计算 Z 和 p 值提供了中介显着性的证据。结果显示，所有中介变量的 Z 值都超过了 0.01 的阈值，表明自变量和因变量之间存在显着的中介效应。换句话说，中介变量影响自变量和因变量之间的关系，应该在该主题的未来研究中予以考虑。
6. Discussion 6. 讨论
This study significantly advances existing literature by shedding light on the intricate relationships between AI assimilation, organizational and customer agility, customer experience, customer relationship quality, and customer performance. The findings not only contribute to the theoretical foundations of these concepts but also reveal their interconnections. Specifically, this study underscores the importance of a profound understanding of AI technology in a business context. Moreover, they emphasize that successful AI assimilation is closely linked to enhanced customer performance, as evidenced by improved sales levels, customer satisfaction, and loyalty. By elucidating these connections, this study provides a nuanced and comprehensive perspective on the implications of AI assimilation that extends and enriches current academic discourse.
这项研究揭示了人工智能同化、组织和客户敏捷性、客户体验、客户关系质量和客户绩效之间错综复杂的关系，极大地推进了现有文献的发展。研究结果不仅有助于奠定这些概念的理论基础，而且揭示了它们之间的相互联系。具体来说，这项研究强调了在商业环境中深刻理解人工智能技术的重要性。此外，他们强调，成功的人工智能同化与提高客户绩效密切相关，销售水平、客户满意度和忠诚度的提高就证明了这一点。通过阐明这些联系，本研究为人工智能同化的影响提供了细致而全面的视角，扩展并丰富了当前的学术话语。
6.1. Theoretical implications
6.1.理论意义
This study makes a highly significant contribution to advancing the understanding of the concepts of AI assimilation, organizational and customer agility, customer experience, customer relationship quality, and, ultimately, customer performance, which all overlap. The results emphasize the need for a deep understanding of AI technology in the business context. As discussed in this paper, the importance of AI assimilation highlights the necessity for companies to master AI technology effectively, integrate it into their operations, and reap the benefits of its adoption. Theories on organizational and customer agility emphasize an organization’s ability to adapt rapidly to market changes and respond to customer needs. This is crucial to maintain and enhance a company’s competitiveness in an ever-changing business environment. Customer experience theory advances the central role of customer experience in shaping the relationship between customers and companies by encompassing factors such as product or service quality, ease of use, and a company’s responsiveness to customer inquiries and complaints. Customer relationship quality theory underscores the importance of relationship quality in influencing customer loyalty and retention, including effective communication, trust, and customer satisfaction. Finally, the customer performance theory clarifies the central role of customer performance in determining a company’s success, including key metrics such as sales level, customer satisfaction, and customer loyalty.
这项研究对于增进对人工智能同化、组织和客户敏捷性、客户体验、客户关系质量以及最终客户绩效等概念的理解做出了非常重要的贡献，这些概念都是重叠的。结果强调需要深入了解商业环境中的人工智能技术。正如本文所讨论的，人工智能同化的重要性凸显了公司有效掌握人工智能技术、将其融入其运营并从中获益的必要性。组织和客户敏捷性理论强调组织快速适应市场变化和响应客户需求的能力。这对于在不断变化的商业环境中保持和增强公司的竞争力至关重要。客户体验理论通过涵盖产品或服务质量、易用性以及公司对客户询问和投诉的响应能力等因素，提升了客户体验在塑造客户与公司之间关系方面的核心作用。客户关系质量理论强调关系质量在影响客户忠诚度和保留方面的重要性，包括有效沟通、信任和客户满意度。最后，客户绩效理论阐明了客户绩效在决定公司成功方面的核心作用，包括销售水平、客户满意度和客户忠诚度等关键指标。
6.2. Managerial implications
6.2.管理影响
This research provides valuable insights for the academic community, as well as significant managerial implications. These findings indicate that effective AI adoption has the potential to influence customer performance positively. In other words, the more companies adopt AI in their operations, the better their customer performance. This implication is crucial for business practitioners as it highlights that AI adoption can be one of the key factors influencing customer performance. Therefore, companies should consider AI adoption strategies to enhance customer performance.
这项研究为学术界提供了宝贵的见解，并具有重要的管理意义。这些发现表明，有效的人工智能采用有可能对客户绩效产生积极影响。换句话说，越多的公司在运营中采用人工智能，他们的客户绩效就越好。这一含义对于商业从业者来说至关重要，因为它强调人工智能的采用可能是影响客户绩效的关键因素之一。因此，公司应该考虑采用人工智能策略来提高客户绩效。
Furthermore, the findings of this research provide insights into the various variables that affect AI assimilation or adoption, which in turn influence the level of AI adoption itself. Factors such as the level of trust in AI, perceptions of usefulness and ease of use, and brand strength have a significant impact on AI adoption. Thus, companies should consider these factors in their efforts to drive the successful adoption of AI. For example, to increase consumer trust in AI, companies must provide comprehensive and transparent technology education. Additionally, companies should design user-friendly and intuitive interfaces to maximize perceptions of the ease of AI use. Finally, they can leverage their brand strength to attract consumer interest in AI. All of these factors are essential in management strategies to facilitate successful AI adoption.
此外，这项研究的结果提供了对影响人工智能同化或采用的各种变量的见解，这些变量反过来又影响人工智能采用本身的水平。对人工智能的信任程度、对有用性和易用性的看法以及品牌强度等因素对人工智能的采用有重大影响。因此，企业在推动人工智能成功采用的过程中应该考虑这些因素。例如，为了增加消费者对人工智能的信任，企业必须提供全面、透明的技术教育。此外，公司应该设计用户友好且直观的界面，以最大限度地提高人工智能使用的易用性。最后，他们可以利用自己的品牌实力来吸引消费者对人工智能的兴趣。所有这些因素对于促进人工智能成功采用的管理策略都是至关重要的。
6.3. Social implications 6.3.社会影响
This study has broader implications in social contexts. With the significant increase in AI use across various industries, understanding its impact on customer performance is crucial. The research results provide essential information for companies to make informed decisions regarding the use of AI and its potential benefits. From the researcher’s perspective, these findings illustrate that effective business practices and successful AI implementation can provide benefits in the form of higher operational efficiency, more personalized customer experiences, and increased customer loyalty. Therefore, companies have significant opportunities to optimize the benefits of AI technology in their efforts to meet or even exceed their customers’ expectations.
这项研究在社会背景下具有更广泛的影响。随着各行业人工智能使用的显着增加，了解其对客户绩效的影响至关重要。研究结果为企业就人工智能的使用及其潜在好处做出明智的决策提供了重要信息。从研究人员的角度来看，这些发现表明，有效的业务实践和成功的人工智能实施可以带来更高的运营效率、更个性化的客户体验和更高的客户忠诚度等好处。因此，企业有很大机会优化人工智能技术的优势，以满足甚至超越客户的期望。
This study also has social impacts in terms of increasing the public understanding of AI and its contribution to creating value for businesses and customers. As AI technology continues to expand in various industries, the public’s understanding of its benefits to businesses and customers has become increasingly important. The results reaffirm that AI is a highly effective tool for businesses to enhance their performance and provide better customer experiences. As AI technology continues to evolve, society must gain a better understanding of AI’s potential to generate value.
这项研究在提高公众对人工智能及其为企业和客户创造价值的贡献方面的理解方面也具有社会影响。随着人工智能技术在各个行业的不断扩展，公众对其给企业和客户带来的好处的理解变得越来越重要。结果再次证明，人工智能是企业提高绩效和提供更好客户体验的高效工具。随着人工智能技术的不断发展，社会必须更好地了解人工智能创造价值的潜力。
6.4. Practical implications
6.4.实际影响
The practical implications of this study are paramount for companies aspiring to master AI. This study unequivocally demonstrates that effective AI adoption positively influences customer performance. As companies navigate the dynamic landscape of AI implementation, understanding the key factors that influence successful assimilation becomes imperative. This study identified critical determinants, including trust in AI, perceptions of usefulness, perceptions of ease of use, and brand strength. This information empowers business practitioners to formulate strategic AI adoption plans that not only consider technological aspects but also address consumer trust, user experience, and brand positioning.
这项研究的实际意义对于渴望掌握人工智能的公司来说至关重要。这项研究明确表明，有效的人工智能采用会对客户绩效产生积极影响。随着公司在人工智能实施的动态格局中前行，了解影响成功同化的关键因素变得势在必行。这项研究确定了关键的决定因素，包括对人工智能的信任、有用性认知、易用性认知和品牌实力。这些信息使商业从业者能够制定战略性人工智能采用计划，该计划不仅考虑技术方面，还考虑消费者信任、用户体验和品牌定位。
To capitalize on the benefits of AI, companies should prioritize building trust among consumers through transparent educational initiatives. Additionally, designing user-friendly interfaces and leveraging strong brand identities can enhance perceptions of the ease of use of AI and attract consumer interest. These practical insights pave the way for companies to tailor their management strategies to ensure a seamless and successful AI adoption process. Thus, this study can serve as a guide for businesses seeking to optimize their performance by strategically integrating AI into their operations.
为了充分利用人工智能的优势，公司应优先考虑通过透明的教育举措在消费者之间建立信任。此外，设计用户友好的界面并利用强大的品牌形象可以增强对人工智能易用性的认知并吸引消费者的兴趣。这些实用见解为公司定制管理策略铺平了道路，以确保无缝且成功的人工智能采用流程。因此，这项研究可以为寻求通过战略性地将人工智能整合到运营中来优化绩效的企业提供指导。
In conclusion, this research not only contributes significantly to the academic literature but also offers tangible and actionable guidance for businesses navigating the AI landscape. By elucidating the theoretical underpinnings and practical implications of AI assimilation, the study equips both scholars and practitioners with a holistic understanding of how mastering AI technology can lead to improved customer performance and overall business success.
总之，这项研究不仅对学术文献做出了重大贡献，而且还为企业在人工智能领域的发展提供了切实可行的指导。通过阐明人工智能同化的理论基础和实际意义，该研究使学者和从业者能够全面了解掌握人工智能技术如何提高客户绩效和整体业务成功。
7. Conclusion 七、结论
This study investigates how the assimilation or adoption of AI affects customer performance. The underlying theories posit that effective AI use positively influences customer performance. To examine the relationships among the variables, we designed a framework and utilized various research variables associated with AI assimilation. The results strongly indicate a significant relationship between well-executed AI implementation and superior customer performance. Proficient AI implementation is correlated with enhanced customer performance across diverse metrics. This research offers concrete evidence that effective AI implementation not only improves operational efficiency for companies but also provides customers with a more convenient and personalized experience when accessing products and services.
本研究调查了人工智能的同化或采用如何影响客户绩效。基本理论认为，有效的人工智能使用会对客户绩效产生积极影响。为了检查变量之间的关系，我们设计了一个框架并利用了与人工智能同化相关的各种研究变量。结果强烈表明，执行良好的人工智能实施与卓越的客户绩效之间存在显着关系。熟练的人工智能实施与不同指标的客户绩效提升相关。这项研究提供了具体的证据，表明有效的人工智能实施不仅可以提高企业的运营效率，还可以为客户在访问产品和服务时提供更加便捷和个性化的体验。
Moreover, this study uncovered an association between sound business practices and higher AI adoption rates. This implies that companies with robust business practices are more inclined to adopt and implement AI. This inclination is grounded in efficient and well-organized structures, processes, and cultures inherent in organizations with sound business practices. Consequently, these organizations are better equipped to integrate AI into various aspects of their businesses. The primary conclusion drawn from this study was that the ability to enhance customer performance through effective AI implementation is of paramount importance. Improved customer performance offers companies the opportunity to achieve higher profits, thereby serving as a significant motivator for AI adoption. Additionally, this study emphasizes that strong business practices are essential prerequisites for the success of AI adoption. Therefore, companies planning to adopt AI should optimize their business practices before entering into AI integration.
此外，这项研究发现了良好的商业实践与更高的人工智能采用率之间的联系。这意味着拥有稳健业务实践的公司更倾向于采用和实施人工智能。这种倾向植根于具有良好业务实践的组织所固有的高效且组织良好的结构、流程和文化。因此，这些组织更有能力将人工智能整合到其业务的各个方面。这项研究得出的主要结论是，通过有效的人工智能实施来提高客户绩效的能力至关重要。客户绩效的提高为公司提供了获得更高利润的机会，从而成为人工智能采用的重要动力。此外，这项研究强调，强有力的商业实践是成功采用人工智能的必要先决条件。因此，计划采用人工智能的企业在进入人工智能集成之前应该优化其业务实践。
However, it is important to acknowledge the limitations of the present study. First, the cross-sectional design limited the ability to draw causal conclusions about the relationships between variables. Future research may benefit from adopting a more suitable longitudinal design to gain insight into the longitudinal impact of AI assimilation. Second, the scope of this study is confined to the impact of AI assimilation on customer service performance and does not encompass its effects on employees within the organisation. Future research could broaden the scope of investigating the impact of AI assimilation on job satisfaction, productivity, and employee well-being. In addition, this study did not delve into the specific algorithms or models used in the investigated AI applications. Subsequent research can provide more detailed insights into AI applications, including the most effective features and algorithms for improving customer satisfaction.
然而，重要的是要承认本研究的局限性。首先，横截面设计限制了得出变量之间关系的因果结论的能力。未来的研究可能会受益于采用更合适的纵向设计，以深入了解人工智能同化的纵向影响。其次，本研究的范围仅限于人工智能同化对客户服务绩效的影响，不包括其对组织内员工的影响。未来的研究可以扩大人工智能同化对工作满意度、生产力和员工福祉影响的调查范围。此外，本研究没有深入研究所研究的人工智能应用中使用的具体算法或模型。后续研究可以提供对人工智能应用更详细的见解，包括提高客户满意度的最有效的功能和算法。
Furthermore, this study offers a general analysis of AI assimilation without exploring the impact of various types of AI applications on customer service satisfaction. Future research could delve into the impact of different AI applications, such as chatbots, voice assistants, and recommendation systems, and identify the types that are most effective in enhancing customer service performance. Finally, the technical challenges associated with AI assimilation, such as data privacy and security issues, complex system integration, and other technical hurdles, were not addressed in this study. Further exploration of these challenges and the proposed solutions is necessary for future research.
此外，本研究提供了人工智能同化的一般分析，但没有探讨各类人工智能应用对客户服务满意度的影响。未来的研究可以深入研究不同人工智能应用程序（例如聊天机器人、语音助手和推荐系统）的影响，并确定最有效提高客户服务绩效的类型。最后，本研究没有解决与人工智能同化相关的技术挑战，例如数据隐私和安全问题、复杂的系统集成和其他技术障碍。未来的研究有必要进一步探索这些挑战和提出的解决方案。
In conclusion, although this research provides a valuable contribution to understanding the impact of AI assimilation on customer service performance, it highlights the need for further exploration to address existing limitations. Future research should consider longitudinal designs, assess the impact of AI assimilation on employees, identify the most effective AI application algorithms and models, explore the impacts of various types of AI applications, and address the technical challenges associated with AI assimilation. Through these endeavors, companies can harness AI technology more effectively to enhance customer satisfaction and overall business performance.
总之，尽管这项研究为理解人工智能同化对客户服务绩效的影响做出了宝贵的贡献，但它强调需要进一步探索以解决现有的局限性。未来的研究应考虑纵向设计，评估人工智能同化对员工的影响，确定最有效的人工智能应用算法和模型，探索各类人工智能应用的影响，解决人工智能同化相关的技术挑战。通过这些努力，企业可以更有效地利用人工智能技术来提高客户满意度和整体业务绩效。
