Understanding the forced adoption of an AI-based health code system in China

Yuan, Jingbo; Shah, Sayed Kifayat; Popp, Jozsef; Acevedo-Duque, Angel Article Understanding the forced adoption of an AI-based health code system in China Amfiteatru Economic Journal Provided in Cooperation with: The Bucharest University of Economic Studies Suggested Citation: Yuan, Jingbo; Shah, Sayed Kifayat; Popp, Jozsef; Acevedo-Duque, Angel (2024) : Understanding the forced adoption of an AI-based health code system in China, Amfiteatru Economic Journal, ISSN 2247-9104, The Bucharest University of Economic Studies, Bucharest, Vol. 26, Iss. 66, pp. 550-567, https://doi.org/10.24818/EA/2024/66/550 This Version is available at: https://hdl.handle.net/10419/300609 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ AE Understanding the Forced Adoption of an AI-Based Health Code System in China 550 Amfiteatru Economic UNDERSTANDING THE FORCED ADOPTION OF AN AI-BASED HEALTH CODE SYSTEM IN CHINA Jingbo Yuan1 , Sayed Kifayat Shah2, Jozsef Popp3 and Angel Acevedo-Duque4 1)2) Shenzhen University, Shenzhen, China 3)WSB University, Poland 4)Public Policy Observatory, Universidad Autonoma de Chile, Santiago, Chile Please cite this article as: Yuan, J., Shah, S.K., Popp, J. and Acevedo-Duque, A., 2024. Understanding the Forced Adoption of an AIBased Health Code System in China. Amfiteatru Economic, 26(66), pp. 550-567. DOI: https://doi.org/10.24818/EA/2024/66/550 Article History Received: 16 November 2023 Revised: 7 February 2024 Accepted: 10 March 2024 Abstract With the growth of technology and the exigency to continuously improve their socioeconomic position, users must gradually adopt new AI-based solutions. However, users may experience dissatisfaction and frustration when faced with the replacement of previous systems. To bridge this gap, this study proposes a theoretical model that relies on the forced acceptance and usage of AI-based services during COVID-19 in China. This research examined the implementation of a novel health code system in which users were forced to exclusively adopt this system to restrict face-to-face interactions. The study hypotheses were evaluated by employing structural equation modelling (SEM) on the data obtained from a survey of 262 Chinese users. The results show that the forced acceptability of use is impacted by technological and personal factors. This study demonstrates the forced implementation and daily utilisation of the health code system to meet the social needs of the vulnerable population and offers a comprehensive analysis of the process by which policies are formulated. This framework will incentivise socioeconomic progress in institutions and society, as well as assist other academicians in organising their thoughts and promoting the development of theory. Keywords: forced acceptance, PLS-SEM model, COVID-19, health-code system, China JEL Classification: O1, O3, I1, M3  Corresponding authors, Sayed Kifayat Shah and Jozsef Popp – e-mail: [email protected].cn and [email protected] This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2024 The Author(s). Economic Interferences AE Vol. 26 • No. 66 • May 2024 551 Introduction The cutting-edge features of technology have changed user behaviour during the very catastrophic COVID-19 outbreak that decimated the whole world (Li et al., 2021). With the choice to remain indoors, the hotel, leisure, and tourism sectors have seen significant negative effects, since individuals were unable to visit or engage in these kinds of businesses and activities. The use of innovative technology is a significant advantage in the modern period in addressing the difficulties posed by epidemics (Shah et al., 2022). Companies have developed and implemented AI-powered contact monitoring tools expeditiously to notify users and public health professionals if anyone has been exposed to the disease and to determine whether a hotspot should be declared. According to the MIT list of such applications, some are transitory, optional, and portable, while others, such as China's health code system, are mandatory, ubiquitous, and intrusive. Previously, mobile ticketing, mobile payment, and other online services have all been used by the high-speed train, airline, public transportation businesses, etc. (Khajeheian and Ebrahimi, 2020). However, the quick creation of an online AI-based “health code system” is one area of actual success that provides users’ whereabouts, contact information, and physiological information through their smartphone. In addition, Chinese pharmacists and companies that were in the forefront of the pandemic quickly introduced other cutting-edge AI-based methods in response to this pandemic. The reopening of industries and the world economy has been made possible using big data and AI technologies for quick and real-time decision-making and social segregation (Li et al., 2021). Today, researchers believe that using AI-based products has advantages for both service providers and users, contributing to future socio-economic development (Meuter et al., 2003). However, users tend to prioritise efficiency and cost-effectiveness. Consequently, they tend to attribute quality improvements to the availability of self-service options and benefit from the notions of autonomy and empowerment (Collier and Kimes, 2013; Reinders et al., 2015). For this purpose, many firms have raised the acuity of the worth of their services, reduced expenditures, and expanded their distribution networks simultaneously by reducing costs to obtain economic benefits (Beatson et al., 2007; Olah et al., 2021). However, the most radical approach of businesses to force consumers to adopt such new products or services is to eliminate the original choice (Cserdi and Kenesei, 2021). The introduction of innovative technological solutions to deliver superior service and improve cost efficiency is an essential feature of these developments for both organisations and final users (Deveci et al., 2019; Chuenyindee et al., 2022). However, introducing new technology, product, or service modes may increase user resistance and lead to a decline in intention and satisfaction (Cao et al., 2021). Therefore, the seamless integration of technological advancements is imperative (Chen et al., 2022; Ullah et al., 2023), which is not uncommon in the health industry. This sector and related users are frequently compelled to maintain only the most effective method, as cost-effectiveness is an absolute necessity. The introduction of novel systems leads to the end of the old system, which often includes the closure of individual and human interaction methods (Reinders et al., 2015; Ma et al., 2019; Cserdi and Kenesei, 2021). As passive recipients of these shifts, related users may believe that deploying the new framework is not in their best interests. Therefore, they are reluctant to accept these changes, although innovative technology-based modes would have been far more socially comfortable and economically beneficial. In these circumstances, it is vital to identify the aspects that may aid user and organisation adoption of novel systems and services. AE Understanding the Forced Adoption of an AI-Based Health Code System in China 552 Amfiteatru Economic The objective of this article is to ascertain the factors that influence the propensity of users to adopt a newly mandated system, the decisions made by firms, and the potential socioeconomic consequences of such systems. An indication of personal social and economic development can be observed in the form of elevated socioeconomic status, measured by attributes such as personal wealth and income. Other major aspects are standard of living, quality of life, and overall health. At the organisational level, socioeconomic growth can be seen in increases in global competitiveness, revenue, consumer needs, company assets, general business potential, and workforce quality. This study emphasises the considerations and perspectives of users regarding the implementation of new service procedures through mandatory usage. Since the COVID-19 pandemic, there has been limited scholarly focus on this specific subject. This study has a distinctive design, as it presents a practical example of using health code systems to develop a novel method of check-up in the COVID-19 environment. This article seeks to examine other factors that might improve the acceptance of new technologies for widespread use, going beyond the fundamental design and focal structures. As this topic has hardly ever been investigated, our findings have implications for businesses in the health industry at both theoretical and practical levels. Theoretically, this study’s findings contribute to the literature by developing an idea of the enforceability of AI technology use and the socio-economic premises of forced adoption. On a practical level, it focuses on innovative health services in China and other health organisations that want to use technological innovations. All businesses that intend to switch to a novel AI-based system without preserving the old one should be conscious of the factors that might aid in the acquisition process. The structure of this article is as follows. Following the introduction, an extensive literature review examines the background of innovative technology. The study outcomes of the forced acceptance of new service systems are also covered in depth in the literature review. Then, based on the literature study, a theoretical model of forced acceptance is presented and assessed employing the structural equation modelling technique. The research’s setting is outlined in the methodology section, followed by an overview of the empirical findings. Finally, the results are evaluated in the discussion and implications section. 1. Literature review 1.1. Health code system The COVID-19 outbreak started in December 2019 in China and had spread to 210 countries worldwide by May 19, 2020, with 4,731,458 cases reported (Miranda et al., 2022). On January 30, 2020, the World Health Organisation (WHO) declared the COVID-19 pandemic a worldwide public health emergency and termed this infection a “pandemic” on March 12, 2020 ( Yi et al., 2020; Miranda et al., 2022). The provision of pharmacy and health services during this pandemic was a challenge that increased the importance of online AI-based services. The quick creation of an online health code system is one area of success in using AI and big data knowledge. This cutting-edge app enabled firms to keep track of a person’s travels, users’ contact records, and biometric information. A strong rivalry between Alibaba and Tencent served as fuel for the creation and implementation of such a product using AI and big data. On February 9, 2020. The two largest corporations in China simultaneously launched and developed their structures in their relevant capital cities. The quick expansion of the health code system throughout the whole country was made possible by competition between the two digital titans and widespread backing from local governments throughout Economic Interferences AE Vol. 26 • No. 66 • May 2024 553 China. At the end of February, the countrywide health code system was embedded into Wechat for the first time. Within a month, this service was used more than 6 billion times among the 900 million WeChat users alone (Tan, 2020). Following the health code system implemented by Tencent and Alipay, which was subsequently approved by the State Council of China, all users of this application are required to provide their personal information, including travel history, contact history, and medical details. By inputting their data, the twodimensional codes with colours categorise their health risk levels. The particular criteria and standard varied among provinces; however, the elementary colour scheme adhered to uniform regulations nationally. The green QR code permitted individuals to travel within the city; yellow indicated the potential risk of needing to isolate at home for 7-14 days; Code Red stipulated 14 days of isolation at home or in a centralised place. After meeting the isolation requirements, the code was automatically returned to green and local travel was allowed again. The development of health information systems made it possible for other firms to make and adopt this type of innovations using AI, big data, and machine learning techniques (Song et al., 2021). 1.2. Forced use of novel technology Service-oriented organisations empower users to perform particular tasks by implementing cutting-edge technologies with cost savings and additional benefits. However, this ideal winwin scenario can only materialise if users effectively deploy and utilise the technology. With either negative or positive rewards, service businesses most frequently influence consumers to use innovative technology (Liljander et al., 2006). Positive inducements include discounts and one-off deals, while negative motivations include the use of penalties and fees that make the initial option less appealing. Forced acceptance and usage represent the utmost extreme situation, in which the opportunity to select a typical product or service is eliminated (Reinders et al., 2015). Punishment tactics, which elicit resistance similar to compulsion, are much less effective than reward systems (Trampe et al., 2014). Therefore, it is apparent that the process of choosing a deployment strategy is intricate and requires significant attention to detail. A business essentially determines to implement compelled use to accelerate the implementation of specific technologies and begin realising efficiency benefits. However, compulsion can alter and hinder user endorsement of a certain technological system. The erosion of intellectual control and the subsequent resistance to consuming the novel service mode is central to explaining this negative effect. The study of Feng et al. (2019) supported the theory of psychological reactance by demonstrating that when airline consumers are required to use self-service check-in vending machines, they will portray this as a risk to their freedom that will cause psychological reactance. This response will result in a pessimistic outlook and the rejection of the new technology product or service (Reinders et al., 2015). Despite users being aware of the advantages associated with technology usage, being forced into adopting an alternative may immediately provoke hostility or negative emotions (Johnson et al., 2008; Cserdi and Kenesei, 2021). Hence, the use of forceful methods diminishes user satisfaction with technology, since it heightens buyer apprehension and anxiety while assessing or using new technological services (Liu, 2012). Furthermore, such innovative technology mandates can lower users’ perceptions of the firm’s overall service quality (Lin and Hsieh, 2007). Reinders et al. (2015) showed in a study of the forced deployment of new technology systems in the Netherlands that even technology professionals have issues that lead to unhappiness and unfavourable word-of-mouth. Therefore, optimal retention of conventional alternatives is required in conjunction with innovative technologies to increase user and company benefits (Oh et al., 2013). Furthermore, businesses can move AE Understanding the Forced Adoption of an AI-Based Health Code System in China 554 Amfiteatru Economic beyond the early opposition and get users used to practising innovative technology systems exclusively for some service aspects. In that case, they will frequently comprehend the advantages of such technologies (Cserdi and Kenesei, 2021). 1.3. Previous studies Most research on technology focuses on the ideas of innovation diffusion (DOI) and the technology adoption model – TAM (Fishbein and Ajzen, 1981). TAM became an essential paradigm applied to adopting a new technology product or service (Schepers and Wetzels, 2007; Belanche et al., 2020). According to TAM, the two primary factors of novel technology acquiescence are the perceived usefulness (PUS), which is the extent to which the consumer considers that employing the system will improve the performance of the assigned activity and the perceived ease of use (PEU), the extent to which the consumer considers the system’s use will not involve extra time and energy. Acceptance is influenced both directly and indirectly by perceived usefulness and ease of use. According to the DOI theory (Rogers et al., 2005), which focuses on the process rather than the outcome, consumer traits distinguish early adopters from late adopters and the elements that facilitate adoption. Based on additional exploration, these user traits can be categorised as technology willingness, desire to engage, perceived risk, or self-efficacy (Cserdi and Kenesei, 2021). This literature review has looked at the consumer acceptance of service technologies on their own or in combination with other service technologies. For example, Lee et al. (2012) evaluated the suitability of the service procedure and the influence of enabling factors on the adoption of such services. The extra elements that substantially impacted the link between intention to use and actual usage were added to the TAM as the model’s foundation. The technology acceptance model frequently augmented by numerous elements was also employed as a theoretical framework in most research on new technology products and services. According to White et al. (2012), the influence of situational elements on technology adoption is frequently more significant than sentiments about these technologies. Based on this, we also extended the TAM with other factors in the context of an innovative health code system service in China. 2. Proposed theoretical model and hypotheses Analysis of prior theoretical and empirical studies in the field of AI technology and required utilisation demonstrates that restricting user access to preferred products or services typically elicits negative user responses. Forcing one service alternative may cause reduced adoption (Feng et al., 2019), unfavourable assessment (Reinders et al., 2015), negative evaluation of the new technology (Liu, 2012), or even migration to a different service (White et al., 2012). In addition, adverse emotional, cognitive, and behavioural effects are possible. When users are forced to use a specific service without alternatives, it might cause anxiety, reduced trust, discomfort, hesitation, or perceived coercions to their independence (Feng et al., 2019). To bridge this gap, we present a framework that focuses on the adoption of novel AI-based services through force and attempts to identify the factors that contribute to this adoption or aversion. Our main concept is the acceptability of businesses’ forceful behaviour to force employees to utilise new alternative services. Therefore, our measurements focus more on the acceptability of forced acceptance rather than just its consequences. In this regard, we aim not only to investigate responses to coercive action, but also the factors that may facilitate its acceptance. Based on the adoption research and proliferation of new technologies, we Economic Interferences AE Vol. 26 • No. 66 • May 2024 555 propose that forced acceptance is influenced by user personality and technological factors. The proposed model (Figure no.1) and the hypotheses are discussed below. Figure no. 1. Proposed theoretical model 2.1. Perceived ease of use (PEU) PEU measures how effortless a user considers it to use a certain piece of technology (Fishbein and Ajzen, 1981). The extent to which users are aware of the intricacy linked to a certain technology will influence its adoption. As a result, the same link is assumed when thinking about forced acceptance; users who think that using an AI health code system is simple would be more inclined to accept it. Therefore, we suggest the following hypothesis: H1: Perceived ease of use is positively correlated with forced acceptance usage. 2.2. Perceived usefulness (PUS) PUS refers to “the level to which a user intuitively feels that using a particular technology will benefit and boost his/her performance” (Davis, 1989). In the context of this study, PUS happens when a user of an AI health code system thinks that it will improve their ability to accomplish their needs. Based on this, we strive to study the relationship between PUS and users’ intentions and put forward the following relevant assumption: H2: Perceived usefulness is positively correlated with forced acceptance usage. 2.3. Perceived trust (PTR) PTR is related to “the product or service providing certificates and labelling as evidence of its safety and reliability” (Mayer et al., 1995; Silva et al., 2017). Grunert et al. (2014) contend that if trust in a product increases, there must be an upsurge in the belief that the product satisfies the required conditions. To create trust, some features are important, such as the existence of the certifier’s, label, and producer’s status, and prestige of the product (Anisimova, 2016). The same connection is thus considered when considering forced acceptance and usage; user who believes utilising a health code system is reliable is more likely to accept it. H3: Perceived trust is positively correlated with force acceptance usage. AE Understanding the Forced Adoption of an AI-Based Health Code System in China 556 Amfiteatru Economic 2.4. Social anxiety (SOA) Fenigstein et al. (1975) describe social anxiety as a feeling of discomfort caused by being aware of others’ opinions of oneself as a social object. In the literature on innovative technology, social anxiety is typically a location regulator identified through crowding perception, alleviating the important link between attitude and intention to practice such technology (Gelbrich and Sattler, 2014). We contend that social anxiety may function as an attribute in addition to being considered just simply as a situational aspect when using such technologies. People’s actions or assessments can vary depending on their tolerance of the stress that comes from having others see them use the health code service; this is especially true when forced introduction is included. Therefore, we propose the following: H4: Social anxiety is positively correlated with forced acceptance usage. 2.5. Self-efficacy (SEY) Bandura (1977) defines SEY as an individual’s opinion of their capability to carry out a specific behaviour. Its theoretical underpinnings are established in social cognition theory, which maintains that SEY views are a major cognitive factor that influences behaviour. Higher SEY boosts technological adoption, as the use of innovative technologies requires confidence in one’s abilities (Limayem et al., 2007; Cserdi and Kenesei, 2021). A health code system adoption is more likely to be a successful procedure, even when used forcibly, the more comfortable and confident, when a person is utilising it. Therefore, we propose the following: H5: Self-efficacy is positively correlated with forced acceptance usage. 2.6. Continuance intentions (CIN) Forcing users to abandon an acceptable mode of a health check system may have a detrimental impact on their perceptions of the organisation through unfavourable word-ofmouth (Reinders et al., 2015; Cserdi and Kenesei, 2021). Users do not consider it equitable for a corporation to push them to perform a task they would not ordinarily do. Furthermore, Bitner et al. (2002) discovered that users may be deeply attached to the initial method of provision manner with which they are familiar. The most hazardous result of compelled practice is the creation of negative perceptions about the product or the firm, which can lead to a reduced level of user retention. Because new technology often enhances service quality, if a corporation can persuade clients to assent and welcome the forced use of newer technologies, it can lead to greater user happiness and continuance intentions (Cserdi and Kenesei, 2021; Zhongjun et al., 2022). As a result, we hypothesise as follows: H6: Forced acceptance is positively correlated with continuance intentions. 3. Methodology This study focused on a revolutionary service system that was implemented in China during the COVID-19 pandemic, using AI and big data. To understand the user’s intent towards this service, an online survey was conducted using convenience sampling methods. A questionnaire was structured based on previous literature related to the new technology services. Based on (Gelbrich and Sattler, 2014; Cserdi and Kenesei, 2021), three questions related to the SOA test were selected for the use of new technology, while the three items relating to SEY were centred on (Webster and Martocchio, 1992). Regarding the Economic Interferences AE Vol. 26 • No. 66 • May 2024 557 characteristics of technology, the three questions used to assess PUS and PEU were used from the Cserdi and Kenesei (2021) work, while the three items used to determine PTR and CIN were based on (Chong et al., 2012; Park et al., 2019) research. Finally, we created a fivepoint scale to assess the acceptability of force acceptance and use as the main study concept. This scale covered users' cognitive, emotional, and behavioural tendencies toward health code service resulting from forced utilisation (Davis, 1989). Next, we used a cautious approach to describe these elements in a way that would allow us to understand the key factors of forced acceptance and consumption. We adopted pre-existing scales from the research to guarantee the reliability of the scales. We employed back-translation Brislin (1970) iteratively and collaboratively to ensure the accuracy of the translations (Shah and Zhongjun, 2021; Mustafa et al., 2022). Two experts translated individually the scales’ components from English to Chinese. After attaining unanimity, the items were back-translated by a third knowledgeable scholar, and after some repetitions, the group once more came to a consensus. Twenty-five people answered the questionnaire as a pilot test. A few small phrasing changes were made to ensure complete clarity based on the pilot test results. There were two sections to the questionnaire. The introduction of the research’s goal was made in the first part, which was then followed by questions on the respondents’ demographics. After the section on the respondents’ attitudes towards new services, forced migration, and continuous intention, general attitude questions about technology use and personal considerations emerged. After that, all the participants were instructed to distribute surveys using the “snowball” approach to friends and were informed that their replies would be kept anonymous and used only for academic purposes to safeguard their identity. The ethics council at Shenzhen University gave the questionnaire its seal of approval. This allowed all participants to provide their consent to fill out the questionnaire. We used nonprobabilistic samples in this article because we were more concerned with basic psychological processes than generalising the population. Structural equation modelling (SEM) methodology permits using non-probabilistic abridged samples. The study aimed to investigate the forced acceptance phenomena rather than the descriptive arrangement of the population (Hair et al., 2016; Shah et al., 2021). A total of 262 respondents were included in the final sample after eliminating incomplete replies (Hair et al., 2017). Table no. 1 provides a thorough description of the sample. The study outcomes exhibited that men (54.9%) and women (45.03%) were involved almost equally, with more than 85% of participants aged 20 to 50 and having a higher education level. Table no. 1. 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