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The Association between Artificial Intelligence Awareness and Employee Depression: The Mediating Role of Emotional Exhaustion and the Moderating Role of Perceived Organizational Support
人工智慧意識與員工憂鬱之間的關聯:情緒疲憊的中介作用與組織支持感知的調節作用

1
School of Business, Nanjing University of Information Science & Technology, Nanjing 210044, China
南京資訊工程大學商學院, 南京 210044
2
School of Business Administration, Shanghai Lixin University of Accounting and Finance, Shanghai 201620, China
上海立信會計金融學院工商管理學院, 上海 201620
3
School of Public Administration, College of Economics and Management, East China Normal University, Shanghai 200062, China
華東師範大學公共管理學院、經濟管理學院, 上海 200062
*
Author to whom correspondence should be addressed.
信件應寄給的作者。
Int. J. Environ. Res. Public Health 2023, 20(6), 5147; https://doi.org/10.3390/ijerph20065147
國際。 J.環境。資源。公共衛生2023 , 20 (6), 5147; https://doi.org/10.3390/ijerph20065147
Submission received: 15 January 2023 / Revised: 8 March 2023 / Accepted: 14 March 2023 / Published: 15 March 2023
收到提交資料:2023年1月15日/修訂:2023年3月8日/接受:2023年3月14日/發布:2023年3月15日
(This article belongs to the Special Issue Mental Health and Wellbeing in Times of Change)
(本文屬於特刊《變革時代的心理健康與福祉》)

Abstract 抽象的

The combination of artificial intelligence (AI) technology with the real economy has dramatically improved the efficiency of enterprises. However, the replacement of AI for employment also significantly impacts employees’ cognition and psychological state. Based on the Conservation of Resources Theory, the relationship between AI awareness and employee depression is explored in this article while examining the mediating role of emotional exhaustion, as well as the moderating role of perceived organizational support. Based on a sample of 321 respondents, the empirical results show that (1) AI awareness is significantly positively correlated with depression; (2) emotional exhaustion plays a mediating role between AI awareness and depression; (3) perceived organizational support negatively moderates the relationship between emotional exhaustion and depression; (4) perceived organizational support negatively moderates the mediating role of emotional exhaustion between AI awareness and depression. The research conclusions provide a theoretical basis for organizations to take measures to intervene in the negative impact of changes in AI technology on employees’ mental health.
人工智慧(AI)技術與實體經濟的結合大大提高了企業的效率。然而,人工智慧替代就業也對員工的認知和心理狀態產生顯著影響。本文基於資源守恆理論,探討了人工智慧意識與員工憂鬱之間的關係,同時檢視了情緒耗竭的中介作用以及感知組織支持的調節作用。基於321位受訪者的樣本,實證結果顯示:(1)AI認知與憂鬱有顯著正相關; (2)情緒耗竭在AI認知與憂鬱之間起中介效果; (3)知覺組織支持負向調節情緒耗竭與憂鬱之間的關係; (4)知覺組織支持負向調節情緒耗竭在AI意識與憂鬱之間的中介效果。研究結論為組織採取措施幹預人工智慧技術變革對員工心理健康的負面影響提供了理論基礎。

1. Introduction 一、簡介

Artificial intelligence (AI) technology has rapidly advanced in recent years and has been widely implemented in various sectors, allowing enormous value to be generated through businesses [1]. However, while the development of AI has given a strong impetus to economic growth and improved the efficiency of economic development, it has also significantly impacted the labor market [2]. Scholars predicted that AI would replace 47% of jobs in the United States in the coming decades [3]. Nearly 55% of front-line manufacturing jobs in China are easily replaceable [4]. In reality, manufacturing enterprises have begun to replace labor with AI machines. As early as 2016, the Foxconn Kunshan Factory replaced 60,000 workers with a batch of AI machines [5]. As some positions will be replaced, AI technology will have a significant impact on employees’ career development, making it an inescapable stressor in contemporary workplaces [6]. In response to this problem, some scholars have called for attention to be paid towards employees’ cognition and coping behavior with AI technology [7]. Brougham and Haar put forward AI awareness to describe the extent to which an employee views the likelihood of AI technology impacting their future career prospects [7]. After this concept was put forward, many scholars studied the influence of AI awareness on employees’ psychological state and behavior. It mainly includes four aspects. First, AI awareness has a positive impact on employees’ psychological state, such as improving employees’ internal work motivation [8]. Second, AI awareness has a negative impact on employees’ psychological state, such as enhancing employees’ job insecurity [9,10], causing employee job burnout [11] and depression [7], reducing the career competency of employees [11], and negatively affecting employees’ organizational identity and career satisfaction [7,11]. Third, AI awareness has a positive impact on employee behavior, such as improving employees’ innovative behavior [8,12,13], promoting employees’ career exploration behavior [14], positively affecting employees’ work engagement [15], and encouraging active learning and task crafting [13]. Fourth, AI awareness has a negative impact on employee behavior, for example, increasing employees’ cynicism and turnover intention [1,6] and promoting employees’ knowledge hiding [16].
近年來,人工智慧(AI)技術迅速發展,並在各個領域中廣泛應用,為企業創造了巨大的價值[ 1 ]。然而,人工智慧的發展在為經濟成長帶來強勁動力、提高經濟發展效率的同時,也對勞動市場產生了顯著影響[ 2 ]。學者預測,未來幾十年人工智慧將取代美國 47% 的工作 [ 3 ]。中國近55%的第一線製造業工作很容易被取代[ 4 ]。現實中,製造業已經開始用人工智慧機器取代勞動力。早在2016年,富士康崑山工廠就用一批人工智慧機器取代了6萬名工人[ 5 ]。隨著部分職位的被取代,人工智慧技術將對員工的職涯發展產生重大影響,成為當代職場中不可避免的壓力源[ 6 ]。針對這個問題,有學者呼籲關注人工智慧技術對員工的認知和應對行為[ 7 ]。 Brougham 和 Haar 提出人工智慧意識來描述員工認為人工智慧技術影響其未來職業前景的可能性的程度[ 7 ]。這個概念提出後,許多學者研究了人工智慧認知對員工心理狀態和行為的影響。主要包括四個方面。首先,人工智慧意識對員工的心理狀態有正面影響,例如提高員工內部工作動機[ 8 ]。 其次,人工智慧意識對員工的心理狀態產生負面影響,如增強員工的工作不安全感[ 9,10 ],造成員工的工作倦怠[ 11 ]和憂鬱[ 7 ],降低員工的職業勝任力[ 11 ],並對員工的組織認同與職業滿意度產生負面影響[ 7 , 11 ]。第三,人工智慧意識對員工行為有正面影響,如提高員工的創新行為[ 8,12,13 ],促進員工的職業探索行為[ 14 ],積極影響員工的工作投入[ 15 ],鼓勵積極主動員工行為第四,人工智慧意識對員工行為有負面影響,例如增加員工的憤世嫉俗和離職傾向[ 1 , 6 ],促進員工的知識隱藏[ 16 ]。
Lazarus theorized that stressors can affect employees’ psychological state and behavior depending on their cognitive appraisal [17]. When stressors are appraised as challenges, individuals will take more positive measures to deal with them, promoting their well-being. However, if stressors are appraised as threats, individuals will take more negative measures to deal with them, harming their well-being [18]. As an important stressor, AI awareness reflects employees’ threat appraisal of AI technology, which may be an important factor affecting their mental health. From the current research, it can be seen that there are still few studies on the relationship between AI awareness and employee mental health. Only Brougham and Haar discussed the correlation between AI awareness and employee depression [7]. However, there is no research on the mediating and moderating mechanisms of AI awareness and employee depression. Depression is an important indicator of mental health, leading to social and occupational dysfunctions [19], bringing significant psychological pain to individuals while seriously endangering their interpersonal relationships, social functions, and quality of life [20]. At the same time, employee depression substantially threatens the normal functioning of an organization through low productivity, employee absenteeism, and poor morale [21]. As a result, the purpose of this article is to discuss the mediating and moderating mechanisms between AI awareness and employee depression. The research significance of this article lies in the following: firstly, it helps to reveal the ways and boundary conditions of the influence of AI awareness on employee depression; second, the research conclusion provides a theoretical foundation for organizations to develop policies to prevent employee depression that may arise during AI implementation.
Lazarus 認為,壓力源可以影響員工的心理狀態和行為,這取決於他們的認知評估 [ 17 ]。當壓力源被視為挑戰時,個人會採取更積極的措施來應對,從而促進他們的福祉。然而,如果壓力源被視為威脅,個人就會採取更多消極措施來應對,從而損害他們的福祉[ 18 ]。人工智慧意識作為重要的壓力源,反映了員工對人工智慧技術的威脅評價,可能是影響員工心理健康的重要因素。從目前的研究可以看出,關於人工智慧認知與員工心理健康關係的研究還較少。只有 Brougham 和 Haar 討論了人工智慧意識與員工憂鬱之間的相關性 [ 7 ]。然而,目前尚未有關於人工智慧認知與員工憂鬱的中介和調節機制的研究。憂鬱症是心理健康的重要指標,導致社會和職業功能障礙[ 19 ],為個體帶來顯著的心理痛苦,同時嚴重危害其人際關係、社會功能和生活品質[ 20 ]。同時,員工憂鬱症會導致生產力低落、員工缺勤和士氣低落,嚴重威脅組織的正常運作[ 21 ]。因此,本文的目的是討論人工智慧認知與員工憂鬱之間的中介與調節機制。 本文的研究意義在於:第一,有助於揭示人工智慧意識對員工憂鬱的影響方式與邊界條件;其次,研究結論為組織制定預防人工智慧實施過程中可能出現的員工憂鬱症的政策提供了理論基礎。
According to Brougham and Haar, AI technology threatens individuals’ total career growth, making it difficult to achieve their career goals. Furthermore, the application of AI technology in an organization will force employees to face the risk of being replaced by AI devices and being undervalued and not regarded highly by their employer, therefore, lowering their organizational status [7]. According to the Conservation of Resources (COR) Theory, people actively acquire, maintain, or protect valuable resources recognized by them. Career development and organizational status are important resources for individuals [22]. When employees realize that AI technology threatens their career development or organizational status, these resources face a considerable risk of depletion, which often leads to the emotional exhaustion of individuals [23], thus increasing the risk of depression in employees [24,25,26]. Therefore, emotional exhaustion may be a key mediating mechanism between AI awareness and employee depression.
布魯厄姆和哈爾認為,人工智慧技術威脅著個人的整體職涯發展,使其難以實現職業目標。此外,人工智慧技術在組織中的應用將迫使員工面臨被人工智慧設備取代、被雇主低估和不重視的風險,從而降低他們的組織地位[ 7 ]。根據資源保護(COR)理論,人們主動獲取、維護或保護他們認可的有價值的資源。職業發展和組織地位是個人的重要資源[ 22 ]。當員工意識到人工智慧技術威脅到他們的職業發展或組織地位時,這些資源面臨相當大的枯竭風險這往往會導致個體的情緒耗竭[ 23 ],從而增加員工抑鬱的風險[ 24,25, 26 ] ]。因此,情緒耗竭可能是人工智慧認知與員工憂鬱之間的關鍵中介機制。
AI awareness is positively related to individual emotional exhaustion, which is viewed as the process of the wearing out and wearing down of a person’s energetic resources [27] and is positively related to depression. However, perceived organizational support, as a psychological resource, would give employees a positive emotional experience [28], which can compensate for the loss of resources in daily work and alleviate the negative impact of the excessive consumption of resources [29,30]. Therefore, perceived organizational support may reduce the correlation between emotional exhaustion and employee depression. Furthermore, perceived organizational support may reduce the indirect correlation between AI awareness and employee depression via emotional exhaustion.
人工智慧意識與個人情緒耗竭呈正相關,情緒耗竭被視為一個人的精力資源消耗和消耗的過程[ 27 ],並且與憂鬱呈正相關。然而,感知組織支持作為一種心理資源,會為員工帶來正面的情緒體驗[ 28 ],可以彌補日常工作中資源的損失,緩解資源過度消耗的負面影響[ 29 , 30 ]。因此,感知到的組織支持可能會降低情緒耗竭與員工憂鬱之間的相關性。此外,感知到的組織支持可能會透過情緒耗竭來減少人工智慧意識與員工憂鬱之間的間接相關性。
Based on the above analysis, a moderated mediation model will be built in this article to analyze the relationship between AI awareness and employee depression, including the mediating effect of emotional exhaustion between AI awareness and employee depression. In addition, we also examined the moderating effect of perceived organizational support on the relationship between emotional exhaustion and employee depression, as well as the indirect relationship between AI awareness and employee depression via emotional exhaustion. The theoretical framework of this article is shown in Figure 1. This research is helpful for deeply understanding the influencing ways and boundary conditions of AI awareness on employee depression, helping enterprises formulate related policies to prevent employee depression and promote AI reform smoothly.
基於上述分析,本文將建構一個有調節的中介模型來分析人工智慧認知與員工憂鬱之間的關係,包括情緒耗竭在人工智慧認知與員工憂鬱之間的中介作用。此外,我們也研究了知覺組織支持對情緒耗竭和員工憂鬱之間關係的調節作用,以及人工智慧意識透過情緒耗竭與員工憂鬱之間的間接關係。本文的理論架構如圖1所示。本研究有助於深入了解人工智慧認知對員工憂鬱的影響方式與邊界條件,幫助企業制定預防員工憂鬱的相關政策,並順利推動人工智慧改革。
Figure 1. The theoretical framework.
圖 1.理論架構。

2. Theory Background and Hypotheses
2 理論背景與假設

2.1. AI Awareness and Depression
2.1.人工智慧意識與憂鬱症

The main features of depression include sadness, guilt or low self-worth, fatigue and poor concentration, loss of pleasure or interests, and poor sleep or appetite [31]. Regarding the risk factors of depression, several researchers have found that losing social and material resources is positively associated with depression [32,33,34].
憂鬱症的主要特徵包括悲傷、內疚或自我價值低落、疲勞和注意力不集中、失去快樂或興趣、睡眠或食慾不佳[ 31 ]。關於憂鬱症的危險因素,一些研究人員發現,失去社會和物質資源與憂鬱症呈正相關[32,33,34 ]
According to the COR Theory, there are four kinds of resources: object resources (such as houses and cars), condition resources (such as marriage, status, and employment), personal resources (such as critical skills and personal traits), and energy resources (such as credit, knowledge, and money) [27].
根據COR理論,資源有四種:客體資源(如房子、汽車)、條件資源(如婚姻、地位、就業)、個人資源(如關鍵技能、個人特質)、精力資源(如信用、知識和金錢) [ 27 ]。
The introduction of AI technology will threaten individual resources, mainly regarding the following aspects. First of all, it may replace some occupations, making it difficult for individuals to achieve their career goals [7], reducing their conditional resources. Secondly, the wage gap for routine/unconventional tasks has widened within organizations [35], leading to a relative decrease in some individuals’ income, which refers to energy resources. Thirdly, the use of a large number of AI machines will change the required skill structure of workers. Workers’ existing skill set is no longer fit for new job requirements, so they need to invest time and energy to learn new skills, which also means a loss of original personal resources. Fourthly, after the application of AI, it may lead to the inconsistency between employees’ knowledge and skills needed for new tasks and their own knowledge and skills, which will have a negative impact on employee’s self-concept–job fit and reduce individuals’ conceptions of the self, such as self-esteem [36]. Self-esteem belongs to personal resources [29]. Therefore, the application of AI will lead to the loss of personal resources. Fifthly, the application of AI makes some workers’ positions in enterprises precarious, which may lead to a perception that employers ignore them and lower their organizational status [7], meaning a loss of condition resources. Sixthly, the replacement of positions by AI applications will cause employees to worry about losing their jobs, generating job-stress-related presenteeism, which siphons off cognitive energy that could otherwise be used to focus on their work [37,38]. In this case, employees’ energy resources also face the possibility of loss.
人工智慧技術的引入將對個體資源產生威脅,主要體現在以下幾個方面。首先,它可能取代一些職業,使個人難以實現職業目標[ 7 ],減少其條件資源。其次,組織內部常規/非常規任務的薪資差距擴大[ 35 ],導致一些個人的收入相對減少,其中涉及能源資源。第三,大量人工智慧機器的使用將改變工人所需的技能結構。工人現有的技能已不再適應新的工作要求,因此需要投入時間和精力來學習新技能,這也意味著原有個人資源的喪失。第四,人工智慧應用後,可能會導致員工新任務所需的知識和技能與自身知識和技能不一致,從而對員工的自我概念——工作契合度產生負面影響,降低個人概念。 [ 36 ]。自尊屬於個人資源[ 29 ]。因此,人工智慧的應用會導致個人資源的流失。第五,人工智慧的應用使一些工人在企業中的地位岌岌可危,這可能會導致雇主忽視他們並降低他們的組織地位[ 7 ],這意味著條件資源的流失。第六,人工智慧應用取代職位將導致員工擔心失去工作,產生與工作壓力相關的出勤主義,從而吸走本來可以用來專注於工作的認知能量[ 37 , 38 ]。 在這種情況下,員工的能源也面臨流失的可能。
The COR Theory holds that the loss of resources will put great pressure on individuals [29]. Stress was a risk factor for depression [39]. Hobfoll et al. posited that resource loss is an important cause of various negative consequences, including depression [40]. Brown and Andrews reported that about 90% of the depression cases they studied were related to losses, except for those that might be a personality disorder [22].
COR理論認為,資源的損失會對個體造成巨大的壓力[ 29 ]。壓力是憂鬱症的危險因子[ 39 ]。霍佈福爾等人。認為資源損失是造成包括憂鬱症在內的各種負面後果的重要原因[ 40 ]。布朗和安德魯斯報告說,他們研究的憂鬱症病例中大約 90% 與損失有關,除了那些可能是人格障礙的病例 [ 22 ]。
Therefore, the greater the threat that AI technology poses to an individual’s career development, the stronger their AI awareness is, the greater the risk of resource loss is, and the higher their risk of depression will be.
因此,人工智慧技術對個人職涯發展的威脅越大,其人工智慧意識越強,資源流失的風險越大,憂鬱的風險也越高。
Based on the above analysis, we put forward hypothesis 1: AI awareness will be positively associated with depression.
基於上述分析,我們提出假設1:AI認知將與憂鬱症呈正相關。

2.2. The Mediating Role of Emotional Exhaustion
2.2.情緒疲憊的中介作用

Emotional exhaustion refers to emotional overwork and exhaustion caused by work, which is manifested as physical fatigue as well as psychological and emotional drain [41]. COR Theory holds that individuals tend to acquire, maintain, and preserve resources, who will be under pressure when faced with the threat of resource loss, actual loss, and failure posed to their acquisition of resources after investing in them [42,43]. Based on the above analysis, the introduction of AI threatens individual career development and makes individuals face a considerable risk of resource loss, becoming an important stressor. As a stressor, the influence of AI on the individual psychological state and behavior depends on their cognitive evaluation. When some individuals evaluate AI technology as a challenge, they are more willing to have a positive response, and if they evaluate AI technology as a threat, they will have a negative response [44]. AI awareness means that individuals think that AI technology threatens their career development. Therefore, the stronger the AI awareness is, the greater the possibility of a negative response will be [7]. When adopting a negative coping style, individuals will want to avoid AI technology, thus reducing their motivation [45]. In this case, the gap between individual work skills and knowledge and the requirements of the new posts after AI changes will become larger, so people’s jobs will be more threatened. As a conditional resource, a job provides compensation and a sense of self-esteem [43]. If the existence of jobs is threatened, the resources can only be made to face the risk of further losses. When individuals think that they are facing a loss of resources and cannot replenish them in time, it will lead to emotional exhaustion [22,46]. Additionally, after the introduction of AI, when realizing that their jobs are threatened, individuals usually have fear and anxiety about their future [47]. These negative emotions further reduce an individual’s ability to save or obtain resources to cope with this situation, leading to more stress, strain, and emotional exhaustion [48]. Therefore, we put forward hypothesis 2: AI awareness will be positively associated with employees’ emotional exhaustion.
情緒耗竭是指工作造成的情緒過度勞累、疲憊不堪,表現為身體疲勞以及心理情緒耗竭[ 41 ]。 COR理論認為,個體傾向於獲取、維持和保存資源,當面臨資源損失、實際損失以及投資資源後獲取資源失敗的威脅時,個體將面臨壓力[ 42 , 43 ]。綜合以上分析,人工智慧的引進威脅到個人職涯發展,使個人面臨相當大的資源流失風險,成為重要的壓力源。作為一種壓力源,人工智慧對個體心理狀態和行為的影響取決於其認知評估。當一些個體將人工智慧技術評為挑戰時,他們更願意做出正面的反應,而如果他們將人工智慧技術評為威脅,他們就會做出負面的反應[ 44 ]。 AI意識是指個人認為AI技術威脅到自己的職涯發展。因此,人工智慧意識越強,出現負面反應的可能性就越大[ 7 ]。當採取消極的因應方式時,個人會想要避開人工智慧技術,進而降低他們的積極性[ 45 ]。在這種情況下,個人的工作技能和知識與人工智慧變革後新職位的要求之間的差距會變得更大,因此人們的工作將受到更大的威脅。作為一種有條件的資源,工作提供報酬和自尊感[ 43 ]。如果就業機會的存在受到威脅,資源只能面臨進一步損失的風險。 當個體認為自己面臨資源流失而無法及時補充時,就會導致情緒耗竭[ 22 , 46 ]。此外,在引入人工智慧後,當意識到自己的工作受到威脅時,個人通常會對自己的未來感到恐懼和焦慮[ 47 ]。這些負面情緒進一步降低了個人儲蓄或獲取資源來應對這種情況的能力,導致更多的壓力、緊張和情緒疲憊[ 48 ]。因此,我們提出假設2:人工智慧認知度與員工情緒耗竭呈正相關。
Emotional exhaustion is conceptualized as representing a deficit in resources, and resource deficits lead to individual psychological problems [49,50]. According to COR Theory, individuals can improve their well-being by preserving and acquiring essential resources. If the key resources are lost, individuals will have destructive emotions, such as tension, stress, and anxiety [50], which are important risk factors for depression [51]. Many studies have shown that emotional exhaustion is highly correlated with depression. It is also found in studies on Chinese students that emotional exhaustion is positively related to depression [52,53]. Depressive disorders are also found to be positively predicted among teachers in primary and secondary schools as well as nurses with emotional exhaustion [54,55]. Therefore, we put forward hypothesis 3: emotional exhaustion will be positively associated with employee depression. Combining Hypothesis 2 with Hypothesis 3, we can put forward Hypothesis 4: emotional exhaustion will mediate the relationship between AI awareness and depression.
情緒耗竭被概念化為資源短缺,而資源短缺會導致個人心理問題[ 49 , 50 ]。根據 COR 理論,個人可以透過保存和獲取必要資源來改善自己的福祉。如果關鍵資源流失,個體就會產生破壞性情緒,如緊張、壓力、焦慮等[ 50 ],這些都是憂鬱症的重要危險因子[ 51 ]。許多研究表明,情緒疲憊與憂鬱症高度相關。對華人學生的研究也發現,情緒疲憊與憂鬱呈正相關[ 52 , 53 ]。研究也發現,情緒疲憊的中小學教師和護理師罹患憂鬱症的機率較高[ 54 , 55 ]。因此,我們提出假設3:情緒耗竭與員工憂鬱呈正相關。結合假設2與假設3,我們可以提出假設4:情緒耗竭會中介AI認知與憂鬱之間的關係。

2.3. The Moderating Role of Perceived Organizational Support
2.3.知覺組織支持的調節作用

Perceived organizational support refers to employees’ general belief that their organizations value their contributions and care for their well-being based on their perception of how organizations reward their work performance and meet their social as well as emotional needs [56]. Perceived organizational support can be regarded as a psychological resource leading to positive emotional experiences because employees feel the support, understanding, and affirmation of their abilities from their colleagues as well as leaders [28]. Emotional exhaustion was viewed as the process of the wearing out and wearing down of a person’s energetic resource [27]. According to COR Theory, psychological resources can compensate for the loss of resources in daily work and alleviate the negative impact of over-consumption [29,30]. Therefore, perceived organizational support can reduce the influence of emotional exhaustion on depression. Studies have also shown that people with higher perceived organizational support experience fewer physical and psychological problems, such as depression and anxiety [57,58].
感知的組織支持是指員工普遍相信,他們的組織重視他們的貢獻並關心他們的福祉,基於他們對組織如何獎勵他們的工作表現和滿足他們的社交和情感需求的看法[ 56 ]。感知的組織支持可以被視為一種導致正向情緒體驗的心理資源,因為員工感受到同事和領導者對他們能力的支持、理解和肯定[ 28 ]。情緒耗竭被視為一個人的精力資源消耗和消耗的過程[ 27 ]。根據COR理論,心理資源可以補償日常工作中資源的損失,並緩解過度消耗的負面影響[ 29 , 30 ]。因此,感知組織支持可以減少情緒耗竭對憂鬱的影響。研究還表明,感知組織支持較高的人經歷的身體和心理問題較少,例如憂鬱和焦慮 [ 57 , 58 ]。
The buffering model of social support also holds that a high degree of social support can buffer the impact of stress on depression [59], while low social support will increase individual susceptibility to depression [60]. Emotional exhaustion is a typical stress response [61]. Therefore, social support can reduce the negative impact of emotional exhaustion on depression [62,63]. The mechanism may be that social support can improve individual self-esteem, thus buffering the effect of emotional exhaustion on individual depression [53,64].
社會支持的緩衝模型也認為,高程度的社會支持可以緩衝壓力對憂鬱的影響[ 59 ],而低度的社會支持會增加個體罹患憂鬱症的易感性[ 60 ]。情緒疲憊是一種典型的壓力反應[ 61 ]。因此,社會支持可以減少情緒耗竭對憂鬱症的負面影響[ 62 , 63 ]。其機制可能是社會支持可以提高個體的自尊,從而緩衝情緒耗竭對個體憂鬱的影響[ 53 , 64 ]。
According to the above analysis, it can be speculated that organizational support, as a social support from organizations, can alleviate the depression caused by emotional exhaustion. The higher the perceived organizational support is, the lower the impact of emotional exhaustion will be on depression. Therefore, we put forward Hypothesis 5: perceived organizational support will negatively moderate the associations between emotional exhaustion and depression.
根據上述分析,可以推測,組織支持作為來自組織的社會支持,可以緩解因情緒耗竭而產生的憂鬱情緒。感知到的組織支持越高,情緒耗竭對憂鬱的影響越低。因此,我們提出假設5:知覺組織支持會負向調節情緒疲憊與憂鬱之間的關聯。
Based on Hypothesis 4 and Hypothesis 5, it can be speculated that the higher the perceived organizational support is, the lower the mediating relationship between AI awareness and depression will be through emotional exhaustion. Therefore, Hypothesis 6 is that perceived organizational support will negatively moderate the mediating role of emotional exhaustion between AI awareness and depression.
根據假設4和假設5,可以推測組織支持感知越高,人工智慧認知與憂鬱之間透過情緒耗竭的中介關係越低。因此,假設6是感知組織支持會負向調節情緒耗竭在人工智慧意識與憂鬱之間的中介作用。

3. Methods 3. 方法

3.1. Procedure and Sample
3.1.程序和樣本

The research data were collected from “Credamo” (https://www.credamo.com/, accessed on 25 December 2022). In order to avoid the influence of common method biases, a two-stage survey with an interval of about two weeks was adopted. The demographic variables, AI awareness, and perceived organizational support of samples were investigated in the first stage. We distributed 692 questionnaires and collected 447 after invalid respondents (identified by trap questions and reverse questions) were eliminated. In the second stage, questionnaires were distributed to the 447 respondents in the first stage, mainly to measure their emotional exhaustion and depression. After eliminating invalid questionnaires, 321 respondents were collected and matched. In terms of the features of respondents, men accounted for 47% and women accounted for 53%. The ages of the respondents were mainly distributed between 20 and 58 years old, and the distribution between 25 and 40 years old was concentrated, accounting for 89.1%. Respondents with a Bachelor’s degree accounted for 80.4%, undergraduates and above accounted for 11.2%, and the others accounted for 8.4%. Respondents came from a variety of occupations, including finance/auditing, management, technology/R&D, human resource management, production workers, clerical/office staff, administration/logistics staff, salespersons, customer service, professionals (such as accountants, lawyers, architects, healthcare workers, journalists), PR people, teachers, etc. With the popularization of AI, these occupational groups are more or less affected by AI applications, thus ensuring the effectiveness of sample selection.
研究資料收集自「Credamo」( https://www.credamo.com/ ,於 2022 年 12 月 25 日造訪)。為了避免共同方法偏差的影響,採取了間隔兩週左右的兩階段調查。第一階段調查了樣本的人口統計變數、人工智慧意識和組織支持感知。我們共發放問卷 692 份,剔除無效受訪者(陷阱問題和反向問題)後回收問卷 447 份。第二階段則發給第一階段的447位受訪者問卷,主要衡量他們的情緒疲憊和憂鬱狀況。剔除無效問卷後,共回收並配對321位受訪者。從受訪者特質來看,男性佔47%,女性佔53%。受訪者年齡主要分佈在20歲至58歲之間,分佈集中在25歲至40歲之間,佔89.1%。受訪者中大學學歷佔80.4%,本科以上佔11.2%,其他佔8.4%。受訪者來自不同職業,包括財務/審計、管理、技術/研發、人力資源管理、生產工人、文員/辦公室人員、行政/後勤人員、銷售人員、客戶服務、專業人士(如會計師、律師、建築師)隨著人工智慧的普及,這些職業群體或多或少受到人工智慧應用的影響,從而保證了樣本選擇的有效性。

3.2. Measures 3.2.措施

The relevant variable measurement scales used in this study are from mature scales used in internationally renowned journals. We translated these scales into Chinese through a back-translation procedure proposed by Brislin [65] and fine-tuned some items according to Chinese daily expression habits. The key variables were scored on a 7-point Likert scale.
本研究所使用的相關變項測量量表均來自國際知名期刊所使用的成熟量表。我們透過 Brislin [ 65 ] 提出的回譯程序將這些量表翻譯成中文,並根據中國人的日常表達習慣對一些項目進行了微調。關鍵變項採用 7 點李克特量表評分。
AI Awareness: The scale developed by Brougham and Haar [7] was adopted, and the original scale was appropriately revised according to the background of AI studied in this article. Specifically, “smart technology, automation, robotics and AI” in the original scale were briefly described as AI. There were four items on the scale, one of which was: “I am personally worried about my future in my organization as AI is replacing employees” (see Appendix A). In this study, Cronbach’s α = 0.91.
AI意識:採用Broughham和Haar[ 7 ]制定的量表,並根據本文研究的AI背景對原量表進行了適當修改。具體來說,原始規模的「智慧技術、自動化、機器人和人工智慧」被簡單地描述為人工智慧。量表上有四個項目,其中之一是:「我個人擔心我在組織中的未來,因為人工智慧正在取代員工」(請參閱附錄 A )。在本研究中,Cronbach's α = 0.91。
Emotional Exhaustion: The scale developed by Watkins et al. [66] was used. There were three items on the scale, one of which was: “I feel burned out from my work” (see Appendix A). In this study, Cronbach’s α = 0.91.
情緒耗竭:沃特金斯等人所發展的量表。使用[ 66 ]。量表上有三個項目,其中之一是:「我對工作感到精疲力竭」(見附錄A )。在本研究中,Cronbach's α = 0.91。
Perceived Organizational Support: The scale adapted by Shanock and Eisenberger [67] was used. There were six items on the scale, one of which was: “My work organization values my contributions to its well-being” (see Appendix A). In this study, Cronbach’s α = 0.82.
知覺的組織支持:使用了 Shanock 和 Eisenberger [ 67 ] 改編的量表。量表共有六個項目,其中之一是:「我的工作組織重視我對其福祉的貢獻」(見附錄 A )。在本研究中,Cronbach's α = 0.82。
Depression: The scale developed by Dhir et al. [68] was adopted. There were five items on the scale, one of which was: “I have felt lonely” (see Appendix A). In this study, Cronbach’s α = 0.87.
憂鬱症:Dhir 等人所製定的量表。 [ 68 ]被採納。量表上有五個項目,其中之一是:「我感到孤獨」(見附錄A )。在本研究中,Cronbach's α = 0.87。
Control Variables: To avoid other variables interfering with the relationship among the core variables in this article, we took individual personality variables as control variables, such as gender, age, education level, and occupation. According to the characteristics of occupations, we divided the sample occupations into physical occupations (production workers), administrative service occupations (administration/logistics staff, clerical/office staff), marketing occupations (salespersons, PR people, customer service), professional occupations (human resources management, finance/auditing, teachers), professionals (such as accountants, lawyers, architects, healthcare workers, journalists), technology research and development occupations (technology/R&D personnel), and management occupations (management). Because the occupation is a classified variable, that is, a nominal variable, its own coding has no practical quantitative relationship, and it only represents the differences between categories. According to Xie [69], in regression analysis, nominal variables cannot be directly included in the regression model as independent variables, and they must be transformed into a set of corresponding virtual variables. Therefore, this article takes the physical occupation as the reference class and uses the other five kinds of virtual variables in the research model. Studies have shown that AI application would affect employees’ work attitude and behavior. There are differences while applying AI in different enterprises, and there are bound to be differences in employees’ work attitude and behavior. Therefore, AI application was taken as a control variable in this article and the measurement of Wang et al. [70] was adopted. There were four items, one of which was: “Compared with manpower, the application range of AI in my unit will be wider and wider.” Cronbach’s α = 0.75.
控制變數:為避免其他變數幹擾本文核心變數之間的關係,我們採用個體人格變數作為控制變量,如性別、年齡、教育程度、職業等。根據職業特點,我們將樣本職業分為體力職業(生產工人)、行政服務​​職業(行政/後勤人員、文員/辦公室人員)、行銷職業(銷售人員、公關人員、客服人員)、專業職業(人力資源管理、財務/審計、教師)、專業人員(如會計師、律師、建築師、醫護人員、記者)、技術研發職業(技術/研發人員)和管理職業(管理)。由於職業是分類變量,即名目變量,其本身的編碼沒有實際的數量關係,僅代表類別之間的差異。謝[ 69 ]認為,在迴歸分析中,名目變數不能作為自變數直接納入迴歸模型,必須將其轉換為一組對應的虛擬變數。因此,本文以體力職業為參考類,在研究模型中採用其他五種虛擬變數。研究表明,人工智慧應用會影響員工的工作態度和行為。不同企業應用人工智慧存在差異,員工的工作態度與行為也必然有差異。因此,本文將人工智慧應用作為控制變量,並採用Wang等人的測量。 [ 70 ]被採納。 有四項,其中一項是:“與人力相比,人工智慧在我單位的應用範圍會越來越廣。”克朗巴赫α = 0.75。

4. Results 4. 結果

4.1. Confirmatory Factor Analysis
4.1.驗證性因素分析

Confirmatory factor analysis was used to test the validity of discrimination among four variables: AI awareness, emotional exhaustion, depression, and perceived organizational support. As shown in Table 1, the data analysis results show that the four-factor model has the best fit with the samples (CFI = 0.94, TLI = 0.93, RMSEA = 0.07, SRMR = 0.04), compared with which other models have a poor fit and have passed the chi-square test with a significance level of 0.001, indicating that the measurement in this study has good discrimination validity.
驗證性因素分析用於測試四個變數之間歧視的有效性:人工智慧意識、情緒耗竭、憂鬱和感知的組織支持。如表1所示,資料分析結果表明,四因素模型與樣本的配適效果最好(CFI = 0.94,TLI = 0.93,RMSEA = 0.07,SRMR = 0.04),相較之下其他模型的適配效果較差擬合並通過了顯著性水準為0.001的卡方檢驗,顯示本研究的測量具有良好的區分效度。
Table 1. Confirmatory factor analysis results.
表1.驗證性因素分析結果。

4.2. Common Method Bias 4.2.共同方法偏差

To avoid the influence of common method bias on the research conclusion, Harman’s single factor test was used to test the common method bias. The results showed that the proportion of the total variance in the first factor accounted for 36.75%, which did not exceed the threshold of 40%, indicating that there was no serious common method bias in the data [71].
為避免共同方法偏差對研究結論的影響,採用哈曼單因子檢定對共同方法偏差進行檢定。結果顯示,第一個因子佔總變異數的比例為36.75%,沒有超過40%的閾值,顯示數據不存在嚴重的共同方法偏差[ 71 ]。

4.3. Descriptive Statistics and Correlation Analysis
4.3.描述性統計和相關性分析

The mean, standard deviations, and correlations among the research variables are shown in Table 2. The results show that there is a significant positive correlation between AI awareness and depression (r = 0.17, p < 0.01), between AI awareness and emotional exhaustion (r = 0.31, p < 0.01), and between emotional exhaustion and depression (r = 0.52, p < 0.01).
研究變項的平均值、標準差和相關性如表2所示。結果表明,AI意識與憂鬱之間存在顯著正相關(r = 0.17, p < 0.01),AI意識與情緒耗竭之間存在顯著正相關(r = 0.31, p < 0.01),情緒耗竭與憂鬱之間有顯著正相關(r = 0.17, p < 0.01)。
Table 2. Descriptive statistics of variables and correlation matrix.
表2.變數和相關矩陣的描述性統計。

4.4. Hypotheses Testing 4.4.假設檢定

Firstly, multiple linear regression was used in this article to test Hypothesis 1, Hypothesis 2, Hypothesis 3, and Hypothesis 4. In the process of regression, the gender, age, education level, AI application, and occupation were taken as control variables. The specific regression results can be seen in Table 3.
本文首先採用多元線性迴歸檢驗假設1、假設2、假設3和假設4。具體迴歸結果見表3
Table 3. Multiple linear regression analysis results.
表3.多元線性迴歸分析結果。
It can be seen from Model 3 that AI awareness was positively associated with employee depression (β = 0.18, p < 0.01); thus, Hypothesis 1 was supported. It can be seen from Model 1 that AI awareness was positively associated with employees’ emotional exhaustion (β = 0.34, p < 0.001); thus, Hypothesis 2 was supported. It can be seen from Model 2 that emotional exhaustion was positively associated with employee depression (β = 0.52, p < 0.001); thus, Hypothesis 3 was supported. It can be seen from Model 4 that emotional exhaustion was positively associated with employee depression (β = 0.51, p < 0.001), but AI awareness was not significantly associated with employee depression again. According to the method proposed by Baron and Kenny [72], it could be judged that emotional exhaustion completely mediated the relationship between AI awareness and employee depression. Thus, Hypothesis 4 was supported.
從模型3可以看出,AI認知與員工憂鬱呈正相關(β = 0.18, p < 0.01);因此,假設1得到支持。從模型1可以看出,AI意識與員工情緒耗竭呈正相關(β = 0.34, p < 0.001);因此,假設2得到支持。從模型2可以看出,情緒耗竭與員工憂鬱呈正相關(β=0.52, p <0.001);因此,假設3得到支持。從模型4可以看出,情緒耗竭與員工憂鬱呈正相關(β = 0.51, p < 0.001),但AI意識又與員工憂鬱不顯著相關。根據Baron和Kenny提出的方法[ 72 ],可以判斷情緒耗竭完全介導了人工智慧認知與員工憂鬱之間的關係。因此,假設4得到支持。
To ensure the robustness of the research results, referring to Hayes’ method [73], we used SPSS PROCESS macro 3.4 to verify the mediating role of emotional exhaustion. In the process, gender, age, education level, occupation, and AI application were taken as control variables. The results showed that the indirect effect of AI awareness on depression via emotional exhaustion was 0.11 and 95%CI = (0.06, 0.17), which did not include 0. Hypothesis 4 was again supported.
為了確保研究結果的穩健性,參考Hayes方法[ 73 ],我們使用SPSS PROCESS宏3.4來驗證情緒耗竭的中介效果。過程中,以性別、年齡、教育程度、職業、人工智慧應用作為控制變數。結果顯示,AI意識透過情緒耗竭對憂鬱症的間接影響為0.11,95%CI=(0.06,0.17),其中不包括0。
Then, according to the general test method, core variables were standardized by converting the raw scores into Z scores, including emotional exhaustion, perceived organizational support, and depression. The regression method was then used to test the moderating role of perceived organizational support, the results of which are shown by Model 5. Emotional exhaustion was positively associated with depression (β = 0.44, p < 0.001), while perceived organizational support was negatively associated with it (β = −0.18, p < 0.05). Then, the interaction term of emotional exhaustion and perceived organizational support was added to the independent variables of Model 5, and depression was regressed. The results showed that the interaction term of emotional exhaustion and perceived organizational support was significantly associated with depression (β = −0.22, p < 0.001), indicating that perceived organizational support moderated the relationship between emotional exhaustion and depression. Hypothesis 5 was supported.
然後,根據通用測驗方法,將原始分數轉換為Z分數,將核心變數標準化,包括情緒耗竭、感知組織支持和憂鬱。接著採用迴歸方法檢驗感知組織支持的調節作用,結果如模型5所示。 0.18, p < 0.05)。然後,將情緒耗竭和感知組織支持的交互項加入模型5的自變數中,對憂鬱進行迴歸。結果顯示,情緒耗竭和感知組織支持的交互項與憂鬱有顯著相關(β = -0.22, p < 0.001),顯示感知組織支持調節情緒耗竭和憂鬱之間的關係。假設5得到支持。
To further clarify the direction and size of the moderating effect, emotional exhaustion, perceived organizational support, and depression were standardized by converting the raw scores into Z scores in this study. The results of the simple slope test showed that when perceived organizational support was smaller, the relationship between emotional exhaustion and depression was positive and significant, and the effect was greater (β = 0.56, p < 0.001); when perceived organizational support was greater, the relationship between emotional exhaustion and depression was positive and significant, but the effect was smaller (β = 0.22, p < 0.01). The specific visualization results are shown in Figure 2, which shows that the higher the degree of perceived organizational support is, the lower the relationship between emotional exhaustion and depression will be, and perceived organizational support negatively moderates the relationship between emotional exhaustion and depression. Hypothesis 5 was supported again.
為了進一步明確調節效果的方向和大小,本研究將原始分數轉換為Z分數,將情緒耗竭、感知組織支持和憂鬱進行標準化。簡單斜率檢定結果顯示,當知覺組織支持較小時,情緒耗竭與憂鬱呈正相關且顯著,且效果較大(β=0.56, p <0.001);當感知到的組織支持較大時,情緒耗竭與憂鬱之間呈正相關且顯著,但效果較小(β = 0.22, p < 0.01)。具體視覺化結果如圖2所示,感知組織支持程度越高,情緒耗竭與憂鬱的關係越低,感知組織支持負向調節情緒耗竭與憂鬱的關係。假設5再次得到支持。
Figure 2. The moderating effect of perceived organizational support.
圖 2.感知組織支持的調節作用。
Then, we used SPSS PROCESS macro 3.4 to test Hypothesis 6, referring to Hayes’ method [73]. In the process, gender, age, education level, AI application, and occupation were taken as control variables. The results are shown in Table 4. When the perceived organizational support was smaller (mean standard deviation), the indirect effect between AI awareness and depression via emotional exhaustion was 0.12, 95%CI = (0.07, 0.19), and the confidence interval did not contain 0, indicating that the indirect effect was significant. When perceived organizational support was greater (mean + standard deviation), the indirect effect between AI awareness and depression via emotional exhaustion was 0.05, 95%CI = (−0.02, 0.12), and the confidence interval contained 0, indicating that the indirect effect was not significant. When the values of perceived organizational support were different, the difference in indirect effect was −0.07, 95%CI = (−0.16, −0.01), and the confidence interval did not contain 0, which indicated that there were significant differences in the indirect effect. The index of moderated mediation = −0.06, 95%CI = (−0.12, −0.01). These results showed that there was a significant moderated mediating effect, and the higher the degree of perceived organization support, the lower the indirect effect between AI awareness and depression via emotional exhaustion. Hypothesis 6 was supported.
然後,我們參考Hayes方法[ 73 ],使用SPSS PROCESS宏3.4來檢定假設6。過程中,以性別、年齡、教育程度、人工智慧應用、職業作為控制變數。結果如表4所示。當感知的組織支持較小(平均標準差)時,AI認知與情緒耗竭憂鬱之間的間接影響為0.12,95%CI=(0.07,0.19),且信賴區間不包含0,顯示間接影響效果顯著。當感知的組織支持較大時(平均值+標準差),AI意識與情緒耗竭憂鬱之間的間接效果為0.05,95%CI = (−0.02, 0.12),信賴區間包含0,表示間接效果並不顯著。當感知組織支持值不同時,間接效果差異為-0.07,95%CI=(-0.16,-0.01),且信賴區間不包含0,表示間接效果有顯著差異。調節中介指數 = −0.06, 95%CI = (−0.12, −0.01)。這些結果表明,存在顯著的調節中介效應,感知組織支持程度越高,人工智慧認知與情緒耗竭憂鬱之間的間接效應越低。假設6得到支持。
Table 4. Moderated mediating effect analysis results.
表 4.調節中介效應分析結果。

5. Discussion 5. 討論

The impact of AI technology on employees was mainly discussed from two aspects in previous studies. First of all, from the perspective of technological application itself, studies examined the impact of AI technology application on employment [3], negative emotions [74], and job insecurity [70]. Secondly, starting from employees’ cognition of threats posed by technological application, studies examined the effect of AI awareness on job insecurity [6], job satisfaction [7], and turnover intention [1]. However, the mediating and moderating mechanism of AI awareness on employee depression was systematically rarely investigated. Based on COR Theory, the relationship between AI awareness and employee depression was discussed in this article while examining the mediating role of emotional exhaustion and the moderating role of perceived organizational support.
過去的研究主要從兩個方面討論人工智慧技術對員工的影響。首先,從科技應用本身的角度,研究檢視了人工智慧科技應用對就業[ 3 ]、負面情緒[ 74 ]和工作不安全感[ 70 ]的影響。其次,從員工對科技應用威脅的認知出發,研究人工智慧認知對工作不安全感[ 6 ]、工作滿意度[ 7 ]和離職傾向[ 1 ]的影響。然而,人工智慧意識對員工憂鬱的中介和調節機制卻很少被系統性地研究。本文基於COR理論,探討了人工智慧認知與員工憂鬱之間的關係,同時檢視了情緒耗竭的中介作用和感知組織支持的調節作用。
Based on a sample of 321 respondents, the empirical results showed that: first, AI awareness was positively associated with employee depression. This conclusion was consistent with that of previous research [7], and it was verified that psychological disorders (such as depression) could also be triggered among employees who perceived AI technology to threaten their career development in a Chinese sample. In addition, it was found in this study that the control variable, AI application, was negatively associated with employee depression, which was inconsistent with the results of previous studies [74]. A reason behind this phenomenon may be that the relationship between AI application and depression is a result of controlling the influence of AI awareness. Therefore, the relationship between AI application and employee depression here is more of a positive relationship formed by the opportunities brought by AI technology to employees’ career development. Second, emotional exhaustion plays a mediating role between AI awareness and employee depression, showing that the threat of AI technology to employees’ mental health mainly comes from the threat of AI changes to employee resources, which is consistent with the results of previous studies, showing that emotional exhaustion plays a mediating role in the process of stress factors and leads to employee depression [53,55]. In addition, when studying the impact of AI awareness on employees’ psychological state and work attitudes, Brougham and Haar posited that AI awareness was related to a series of negative consequences [7]. However, they did not confirm this mechanism based on data, and the conclusions of this article indirectly supported their inferences. Third, perceived organizational support negatively moderated the relationship between emotional exhaustion and depression. Furthermore, perceived organizational support negatively moderated the mediating effect of emotional exhaustion on the relationship between AI awareness and depression. This conclusion confirmed the view of COR Theory [29,30] that organizational support, as a resource, could supplement the resource loss of employees and reduce the negative impact of resource loss on employees’ stress reactions. In addition, as a form of social support, organizational support could function as a stress buffer and reduce the impact of stressful events on depression. The conclusion of this article also confirms the Stress Buffer Theory of social support [1].
基於321名受訪者的樣本,實證結果顯示:第一,人工智慧認知與員工憂鬱呈正相關。這個結論與先前的研究一致[ 7 ],並且在中國樣本中證實,認為人工智慧技術威脅其職業發展的員工也可能引發心理障礙(如憂鬱症)。此外,本研究發現控制變項AI應用與員工憂鬱呈負相關,這與先前的研究結果不一致[ 74 ]。這現象背後的一個原因可能是人工智慧應用與憂鬱症之間的關係是控制人工智慧意識影響的結果。因此,這裡的人工智慧應用與員工憂鬱的關係更多的是人工智慧技術為員工職涯發展帶來的機會所形成的正向關係。其次,情緒耗竭在AI意識與員工憂鬱之間起中介作用,顯示AI技術對員工心理健康的威脅主要來自於AI變化對員工資源的威脅,這與以往的研究結果一致,顯示認為情緒耗竭在應在激因素過程中扮演中介角色並導致員工憂鬱[ 53 , 55 ]。此外,在研究人工智慧意識對員工心理狀態和工作態度的影響時,Broughham和Haar認為人工智慧意識與一系列負面後果有關[ 7 ]。但他們並沒有根據數據來證實這個機制,而這篇文章的結論也間接支持了他們的推論。 第三,感知到的組織支持對情緒疲憊和憂鬱之間的關係有負向調節效果。此外,感知到的組織支持負向調節了情緒耗竭對人工智慧意識和憂鬱之間關係的中介作用。這個結論證實了COR理論[ 29 , 30 ]的觀點,即組織支持作為一種資源,可以補充員工的資源損失,減少資源損失對員工壓力反應的負面影響。此外,作為社會支持的一種形式,組織支持可以起到壓力緩衝的作用,減少壓力事件對憂鬱的影響。本文的結論也證實了社會支持的壓力緩衝理論[ 1 ]。

5.1. Theoretical Implications
5.1.理論意義

A moderated mediation model based on COR Theory is constructed in this research to explore the relationship between AI awareness and depression. The research conclusion has important theoretical significance for understanding the relationship mechanism between employees’ stress cognition and emotional response with the changes in AI technology.
本研究建構了基於COR理論的有調節的中介模型,以探討人工智慧認知與憂鬱之間的關係。研究結論對於理解人工智慧技術變革下員工壓力認知與情緒反應的關係機制具有重要的理論意義。
First of all, previous scholars have discussed the relationship between AI awareness and employee depression based on a career-planning model [7]. However, they did not discuss the mediating mechanism between AI awareness and employee depression. In this article, the mediating mechanism of relation construction between AI awareness and employee depression is discussed from the perspective of COR Theory. In doing so, we expanded the interpretive perspective on the relationship between AI awareness and employee depression and an increased understanding of the mediating mechanisms between them.
首先,以往學者基於職涯規劃模型討論了人工智慧認知與員工憂鬱之間的關係[ 7 ]。然而,他們並沒有討論人工智慧認知與員工憂鬱之間的中介機制。本文從COR理論的觀點探討了人工智慧認知與員工憂鬱關係建構的中介機制。在這個過程中,我們擴大了對人工智慧意識與員工憂鬱之間關係的解釋視角,並加深了對它們之間中介機制的理解。
Secondly, previous research lacked an examination of the buffering effect of organizational support in the relationship between AI awareness and employee depression, and only some research discussed the direct effect of organizational support on depression [30]. This research confirms the positive role of perceived organizational support in the process of employee stress relief and expands the cognition of boundary conditions between AI awareness and employee depression in the context of AI technological application.
其次,過去的研究缺乏對組織支持在人工智慧認知與員工憂鬱關係中的緩衝作用的檢驗,只有部分研究討論了組織支持對憂鬱的直接影響[ 30 ]。本研究證實了感知組織支持在員工壓力緩解過程中的正面作用,拓展了人工智慧技術應用背景下人工智慧意識與員工憂鬱邊界條件的認知。
Thirdly, the influence of AI awareness on employee depression was explored in a previous study [7]. However, the mechanism behind the relationship between AI awareness and employee depression has not been systematically investigated. A systematic model is put forward in this article to analyze the mediating and moderating mechanism between AI awareness and employee depression. Through analysis, the conclusion can help to deeply understand the impact and boundary condition between AI awareness and employee depression, which provide a theoretical basis for organizations to take measures to intervene in employee depression with changes in AI.
第三,先前的研究探討了人工智慧意識對員工憂鬱的影響[ 7 ]。然而,人工智慧認知與員工憂鬱之間關係背後的機制尚未被系統性地研究。本文提出系統模型來分析人工智慧認知與員工憂鬱之間的中介與調節機制。透過分析,該結論有助於深入理解人工智慧認知與員工憂鬱之間的影響和邊界條件,為組織利用人工智慧的變化採取措施幹預員工憂鬱提供理論基礎。

5.2. Management Implications
5.2.管理意義

The integration of AI in industry can provide a market space for AI and improve the efficiency of the economy. However, it must be considered that the replacement of employment by AI technology will have a series of adverse effects on employees’ cognition and psychological state, which may become an important obstacle to the smooth implementation of this integration process. Therefore, measures must be taken to guide employees to correctly recognize the technological changes in AI, ease psychological problems arising from the process, ensure the support of employees for the changes in AI, and create a powerful internal environment for organizations to implement AI. The theoretical research results of this article provide a theoretical basis for policy making in organizations.
人工智慧與工業的融合可以為人工智慧提供市場空間,提高經濟效率。但必須考慮到,人工智慧技術取代就業將會對員工的認知和心理狀態產生一系列不利影響,這可能成為這項融合進程順利實施的重要障礙。因此,必須採取措施引導員工正確認識人工智慧技術變革,緩解過程中產生的心理問題,確保員工對人工智慧變革的支持,為組織實施人工智慧創造強大的內部環境。本文的理論研究成果為組織決策提供了理論基礎。
First of all, the research holds that AI awareness will increase employee depression. This conclusion shows that employees’ cognition significantly influences their psychological state. When employees perceive that AI is more threatening to their career development, their degree of depression is higher. Therefore, it is necessary to strengthen and guide employees to correctly recognize the influence of AI technology on career development through publicity. At the same time, companies should encourage employees to actively pay attention to the opportunities brought by AI technology to employees’ career development. Existing research also shows that with the introduction of AI technology, new employment opportunities will be created [75] while improving the job quality of employees [76]. Encouraging employees to actively recognize the opportunities for career development based on AI technology can make them actively respond to the impact of AI technology, thereby improving their career satisfaction and happiness as well as reducing their depression.
首先,研究認為人工智慧意識會增加員工的憂鬱程度。這一結論表明,員工的認知對其心理狀態有顯著影響。當員工認為人工智慧對他們的職涯發展威脅更大時,他們的憂鬱程度就更高。因此,有必要透過宣傳加強和引導員工正確認識人工智慧技術對職涯發展的影響。同時,企業應鼓勵員工積極關注人工智慧技術為員工職涯發展帶來的機會。現有研究也表明,隨著人工智慧技術的引入,將創造新的就業機會[ 75 ],同時提高員工的工作品質[ 76 ]。鼓勵員工主動認識以人工智慧技術為基礎的職涯發展機會,可以使他們積極應對人工智慧技術的影響,從而提高職業滿意度和幸福感,減少憂鬱情緒。
Secondly, the research shows that emotional exhaustion is an important mediating mechanism between AI awareness and employee depression. According to COR Theory, in the face of AI technological changes, although opportunities and threats coexist, due to the uncertainty of opportunities, employees tend to conserve resources to respond to threats rather than investing in them to respond to opportunities [33]. In this case, it is impossible for employees to keep up with the pace of AI technology, who are gradually eliminated with AI changes, encountering a gradual loss in resources and making them fall into a loss spiral. Therefore, to avoid this scenario, companies must develop policies that guide employees to actively invest resources in response to AI changes, for example, changing knowledge and skill structures through learning. In addition, employees should improve their own self-efficacy regarding adapting to AI and learning the necessary new skills, so as to help them better master the knowledge and skills to adapt to AI to improve their job security, income, attendance, and organizational status, so as to reduce losses in resources and prevent emotional exhaustion.
其次,研究顯示情緒耗竭是人工智慧認知與員工憂鬱之間的重要中介機制。根據COR理論,面對人工智慧技術變革,雖然機會與威脅並存,但由於機會的不確定性,員工傾向於保存資源來應對威脅,而不是投資資源來應對機會[ 33 ]。在這種情況下,員工不可能跟上AI技術的步伐,隨著AI的變革逐漸被淘汰,資源逐漸流失,陷入虧損螺旋。因此,為了避免這種情況,企業必須制定政策,引導員工積極投入資源來應對人工智慧的變化,例如透過學習改變知識和技能結構。此外,員工還應提高自身適應人工智慧的自我效能感,學習必要的新技能,幫助員工更好地掌握適應人工智慧的知識和技能,提高工作保障、收入、出勤率和組織地位。資源損失,防止情緒耗竭。
Thirdly, the study has shown that perceived organizational support can effectively alleviate the indirect effect between AI awareness and depression via emotional exhaustion. This conclusion shows that although the threats posed by AI to employees’ career development will lead to emotional exhaustion, perceived organizational support, as an important psychological resource, helps employees recover from resource depletion. Therefore, organizations should strengthen their support for employees, recognize the efforts made by them in the process of AI transformation, and give them positive feedback. Meanwhile, organizations should formulate policies to help employees adapt to AI technological changes and enable them to strengthen their self-efficacy to such changes.
第三,研究顯示感知組織支持可以透過情緒耗竭有效緩解人工智慧意識與憂鬱之間的間接影響。這個結論表明,雖然人工智慧對員工職涯發展的威脅會導致員工情緒耗竭,但感知組織支持作為重要的心理資源,有助於員工從資源枯竭中恢復過來。因此,組織應加強對員工的支持,認可他們在人工智慧轉型過程中所做的努力,並給予他們正面的回饋。同時,組織應制定政策以幫助員工適應人工智慧技術變革,並增強員工應對變革的自我效能感。

5.3. Limitations and Future Research Directions
5.3.局限性和未來的研究方向

Although a theoretical framework is proposed to preliminarily explore the relationship mechanism of AI awareness and employee depression in this study, there are still several deficiencies that need to be further improved in the follow-up research.
雖然本研究提出了一個理論架構來初步探討人工智慧認知與員工憂鬱的關係機制,但仍存在一些不足,需要後續研究進一步完善。
First of all, although a two-stage research method was used in this study to avoid common method bias, the core variables of emotional exhaustion and depression were still investigated simultaneously, making it possible that common method biases interfered with the research. In order to make the research results more objective, the use of the three-stage research method can be considered in follow-up research in the future, in which AI awareness, emotional exhaustion, and depression are evaluated by employees at three timepoints respectively.
首先,雖然本研究採用了兩階段的研究方法來避免共同方法偏差,但仍同時檢視情緒耗竭和憂鬱這個核心變量,使得共同方法偏差有可能幹擾研究。為了使研究結果更客觀,後續研究中可以考慮採用三階段研究方法,分別在三個時間點對員工的人工智慧認知、情緒疲憊和憂鬱進行評估。
Secondly, in the aspect of sample information collection, we collected the occupational information of the sample, ignoring the information of the industry where the sample is located. In future research on the relationship between AI awareness and depression, we can consider collecting information on the industry in which the samples are located, and also including the industry as a control variable in the research model to discuss whether the industry will affect the research conclusion. In addition, the existing research on the influence of AI awareness on employees’ psychological state and behavior often locks the research object in a specific industry, such as the service industry [11]. Future research can also focus on special industries when discussing the relationship between AI awareness and depression.
其次,在樣本資訊收集方面,我們收集了樣本的職業信息,忽略了樣本所在行業的資訊。未來研究AI認知與憂鬱的關係時,可以考慮收集樣本所在行業的信息,同時將行業作為控制變量納入研究模型中,討論行業是否會影響研究結論。此外,現有的人工智慧認知對員工心理狀態和行為影響的研究往往將研究對象鎖定在特定產業,例如服務業[ 11 ]。未來的研究在討論人工智慧意識與憂鬱症的關係時也可以關注特殊產業。
Thirdly, the relationship between AI awareness and employee depression is discussed in this article. AI awareness refers to how employees perceive that AI technology will threaten their career development [7]. Lazarus and Folkman believe that stressors impact employees’ psychological state and behavior depending on their cognition, including challenge and threat evaluations. Different cognitive evaluations on stress lead to different behavioral responses, which have different long-term effects on individuals [18]. Generally speaking, when evaluating stress as a threat, individuals are more likely to take a negative behavioral response; when evaluating stress as a challenge, they are more likely to have a positive behavioral response. This article is focused on the relationship between individual threat evaluations on AI technology and depression, but that between individual challenge evaluations on AI technology and depression is not investigated. In fact, according to the Transactional Theory of Stress, when evaluating AI technology as a challenge, individuals will be more inclined to take active measures to obtain more resources, which can theoretically alleviate depression. Therefore, we can further explore the relationship between individual challenge evaluations on AI technology and depression in the future.
第三,本文討論了人工智慧意識與員工憂鬱症之間的關係。人工智慧意識是指員工如何看待人工智慧技術將威脅他們的職業發展[ 7 ]。拉札勒斯和福克曼認為,壓力源會根據員工的認知(包括挑戰和威脅評估)來影響他們的心理狀態和行為。對壓力的不同認知評估會導致不同的行為反應,進而對個體產生不同的長期影響[ 18 ]。一般來說,當壓力視為威脅時,個人更有可能採取消極的行為反應;當壓力視為挑戰時,他們更有可能做出積極的行為反應。本文主要研究人工智慧技術的個別威脅評估與憂鬱症之間的關係,但沒有研究人工智慧技術的個別挑戰評估與憂鬱症之間的關係。事實上,根據壓力交易理論,當將人工智慧技術評為一種挑戰時,個體會更傾向於採取積極措施來獲取更多資源,這在理論上可以緩解憂鬱症。因此,未來我們可以進一步探討個體對AI技術的挑戰評估與憂鬱症之間的關係。
Fourthly, although the mediating role of emotional exhaustion in the relationship between AI awareness and depression is examined according to COR Theory in this article, there may be other mediating mechanisms. Job insecurity is predicted based on AI awareness [6], which is associated with depression [77]. Since the application of AI has a negative impact on employees’ self-concept–job fit [36], it may negatively affect depression through the meaning of work [78]. Therefore, job insecurity and threatened self-concept–job fit may be essential mediating mechanisms between AI awareness and depression. In the future, we can consider further exploring the mediating roles of job insecurity and threatened self-concept–job fit between AI awareness and depression.
第四,雖然本文根據COR理論檢視了情緒耗竭在AI意識與憂鬱關係中的中介作用,但可能還有其他中介機制。工作不安全感是根據人工智慧意識來預測的[ 6 ],這與憂鬱症有關[ 77 ]。由於人工智慧的應用對員工的自我概念-工作契合度產生負面影響[ 36 ],因此可能透過工作意義對憂鬱產生負面影響[ 78 ]。因此,工作不安全感和自我概念-工作契合度受到威脅可能是人工智慧意識和憂鬱之間重要的中介機制。未來,我們可以考慮進一步探討工作不安全感和威脅性自我概念-工作契合度在人工智慧意識與憂鬱之間的中介作用。
Fifthly, only the moderating effect of perceived organizational support between AI awareness and depression is examined in this article, ignoring other factors that can buffer the relationship between AI awareness and depression. Perceived organizational support, as a psychological resource from the outside world, can help individuals cope with stressful situations. However, in addition to the external resource support, internal resources can also play this role, such as individual self-efficacy and self-esteem, which can enable individuals to utilize a more active method to deal with the situation of emotional exhaustion brought by AI awareness, thus obtaining the supplement of resources while alleviating the negative impact of emotional exhaustion. In the future, we can further explore the moderating role of self-efficacy or self-esteem between AI awareness and employee depression.
第五,本文僅檢視了感知組織支持在人工智慧認知與憂鬱之間的調節作用,忽略了其他可以緩衝人工智慧認知與憂鬱之間關係的因素。感知組織支持作為來自外在世界的心理資源,可以幫助個人處理壓力情境。然而,除了外在資源支援外,內在資源也可以發揮這種作用,例如個體的自我效能感和自尊,可以使個體以更積極的方式來應對人工智慧帶來的情緒耗竭的情況。資源的補充,同時緩解情緒耗竭的負面影響。未來,我們可以進一步探討自我效能或自尊在人工智慧認知與員工憂鬱之間的調節作用。

6. Conclusions 6. 結論

According to COR Theory, the relationships between AI awareness and employee depression, the mediating role of emotional exhaustion, and the moderating role of perceived organizational support were explored in the current study. We found that AI awareness was positively associated with employee depression, emotional exhaustion played a mediating role in the relationship between AI awareness and depression, and employees’ perceived organizational support could alleviate the mediating role of emotional exhaustion between AI awareness and depression. Ultimately, we recommend that organizations take steps to mitigate the adverse effect of AI technology changes on employees’ mental health.
根據COR理論,本研究探討了人工智慧意識與員工憂鬱之間的關係、情緒耗竭的中介作用以及感知組織支持的調節作用。我們發現人工智慧認知與員工憂鬱呈正相關,情緒耗竭在人工智慧認知與憂鬱之間發揮中介作用,員工感知的組織支持可以減輕情緒耗竭在人工智慧認知與憂鬱之間的中介作用。最終,我們建議組織採取措施減輕人工智慧技術變革對員工心理健康的不利影響。

Author Contributions 作者貢獻

Conceptualization, G.X.; methodology, G.X., M.X. and J.Z.; formal analysis, G.X. and M.X.; investigation, G.X., M.X. and J.Z.; writing—original draft preparation, G.X.; writing—review and editing, G.X., M.X. and J.Z.; funding acquisition, G.X. All authors have read and agreed to the published version of the manuscript.
概念化,GX;方法論、GX、MX 和 JZ;形式分析、GX 和 MX;調查、GX、MX 和 JZ;寫作——初稿準備,GX;寫作-審查和編輯,GX、MX 和 JZ;資金收購,GX 所有作者均已閱讀並同意稿件的出版版本。

Funding 資金

This work was funded by The National Social Science Fund of China, grant number “20CSH080”.
該工作由國家社科基金資助,項目編號「20CSH080」。

Institutional Review Board Statement
機構審查委員會聲明

The study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the University Committee on Human Research Protection, East China Normal University (HR 796-2022).
該研究是根據《赫爾辛基宣言》的指導原則進行的,並得到了華東師範大學人類研究保護大學委員會的批准(HR 796-2022)。

Informed Consent Statement
知情同意書

Informed consent was obtained from all subjects involved in the study.
參與研究的所有受試者均獲得了知情同意。

Data Availability Statement
數據可用性聲明

The data presented in this study are available on request from the corresponding author.
本研究提供的數據可根據通訊作者的要求提供。

Conflicts of Interest 利益衝突

The authors declare no conflict of interest.
作者宣稱沒有利益衝突。

Appendix A 附錄A

Table A1. Measurement items of key variables.
表 A1。關鍵變數的測量項目。
Table A1. Measurement items of key variables.
VariablesItemsSources
Artificial Intelligence(AI) Awareness1I think my job could be replaced by AIBrougham and Haar (2018) [7]
2I am personally worried that what I do now in my job will be able to be replaced by AI
3I am personally worried about my future in my organisation due to AI replacing employees
4I am personally worried about my future in my industry due to AI replacing employees
Emotional Exhaustion 1I feel emotionally drained from my workWatkins et al. (2014) [66]
2I feel burned out from my work
3I feel exhausted when I think about having to face another day on the job.
Perceived Organizational Support 1The organization values my contribution to its well-beingShanock & Eisenberger(2006) [67]
2The organization strongly considers my goals and values
3The organization really cares about my well-being
4The organization is willing to help me when I need a special favor
5The organization shows very little concern for me
6The organization takes pride in my accomplishments at work
Depression1I have felt lonelyDhir et al. (2018) [68]
2I did not enjoy my life
3I have felt myself unworthy
4I have felt all the joy had disappeared from my life
5I have felt my sadness was not relieved even with help of family/friends

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Figure 1. The theoretical framework.
Ijerph 20 05147 g001
Figure 2. The moderating effect of perceived organizational support.
Ijerph 20 05147 g002
Table 1. Confirmatory factor analysis results.
ModelDescriptionχ2df△χ2△dfCFITLIRMSEASRMR
1Four-factor model317.75129 0.940.930.070.04
2Three-factor model1016.92132699.18 ***30.730.690.150.14
3Three-factor model1079.32132761.58 ***30.710.660.150.17
4Three-factor model704.60132386.86 ***30.820.800.120.07
5Two-factor model1429.021341111.27 ***50.600.550.170.19
6Two-factor model1527.021341209.28 ***50.570.510.180.14
7Single-factor model1758.551351440.80 ***60.500.440.190.14
1. It is a hypothetical model. 2. AI awareness and emotional exhaustion are combined as one factor. 3. AI awareness and depression are combined as one factor. 4. Emotional exhaustion and depression are combined as one factor. 5. AI awareness and depression are combined as one factor; emotional exhaustion and perceived organizational support are combined as one factor. 6. AI awareness, emotional exhaustion, and depression are combined as one factor. 7. All variables are combined as one factor. △χ2 tests relative to Model 1. ***, represent p < 0.001.
Table 2. Descriptive statistics of variables and correlation matrix.
VariablesMSD1234567
1. Gender0.470.50
2. Age30.545.370.10
3. Education5.020.580.080.02
4. AI application5.620.800.090.17 **0.09
5. AI awareness3.031.36−0.000.100.010.06
6. Emotional exhaustion2.871.37−0.02−0.12 *−0.06−0.29 **0.31 **
7. Perceived organizational support5.760.650.12 *0.070.14 *0.28 **−0.45 **−0.49 **
8. Depression1.930.87−0.02−0.10−0.00−0.16 **0.17 **0.52 **−0.40 **
* p < 0.05; ** p < 0.01; two-tailed tests. SD = standard deviation. Gender is coded 1 = male and 0 = female; Education level is coded 1 = primary school or below, 2 = junior high school, 3 = general high school/secondary school/technical school/vocational high school, 4 = college graduates, 5 = university graduates, 6 = Master, 7 = Ph.D.
Table 3. Multiple linear regression analysis results.
Emotional ExhaustionDepression
Model 1Model 2Model 3Model 4Model 5Model 6
Gender0.030.000.020.000.020.04
Age−0.09 *−0.05−0.10−0.05−0.05−0.05
Education level−0.050.050.020.050.060.04
AI application−0.28 ***−0.01−0.15 **−0.010.02−0.01
Occupations: Physical occupation
Administrative service occupation0.16 *0.17*0.26 **0.17 *0.15 *0.16 *
Marketing occupation0.000.010.010.010.010.02
Professional occupation0.19 *0.030.130.030.030.05
Technology research and development occupation0.24 **−0.020.11−0.02−0.010.02
Management occupation0.060.080.110.080.080.09
AI awareness0.34 *** 0.18 **0.01
Emotional exhaustion 0.52 *** 0.51 ***0.44 ***0.39 ***
Perceived organizational support −0.18 **−0.16 **
Emotional exhaustion×perceived organizational support −0.22***
R20.25 ***0.31 **0.11 **0.31 ***0.33 ***0.37 ***
ΔR2 0.20 *** 0.04 ***
F10.1113.743.7812.4613.7515.17
* p < 0.05; ** p < 0.01; *** p < 0.001; two-tailed tests.
Table 4. Moderated mediating effect analysis results.
AI Awareness → Emotional Exhaustion → Depression
Perceived Organizational SupportEffectSE95% Confidence Interval
LLCIULCI
Low perceived organizational support0.120.030.070.19
High perceived organizational support0.050.04−0.020.12
Differences between high and low levels of perceived organizational support−0.070.04−0.16−0.01
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MDPI and ACS Style MDPI 和 ACS 風格

Xu, G.; Xue, M.; Zhao, J. The Association between Artificial Intelligence Awareness and Employee Depression: The Mediating Role of Emotional Exhaustion and the Moderating Role of Perceived Organizational Support. Int. J. Environ. Res. Public Health 2023, 20, 5147. https://doi.org/10.3390/ijerph20065147
徐,G。薛,M。趙傑。國際。 J.環境。資源。公共衛生2023 , 20 , 5147。

AMA Style AMA風格

Xu G, Xue M, Zhao J. The Association between Artificial Intelligence Awareness and Employee Depression: The Mediating Role of Emotional Exhaustion and the Moderating Role of Perceived Organizational Support. International Journal of Environmental Research and Public Health. 2023; 20(6):5147. https://doi.org/10.3390/ijerph20065147
徐剛,薛明,趙傑。國際環境研究和公共衛生雜誌。 2023; 20(6):5147。 https://doi.org/10.3390/ijerph20065147

Chicago/Turabian Style 芝加哥/圖拉比安風格

Xu, Guanglu, Ming Xue, and Jidi Zhao. 2023. "The Association between Artificial Intelligence Awareness and Employee Depression: The Mediating Role of Emotional Exhaustion and the Moderating Role of Perceived Organizational Support" International Journal of Environmental Research and Public Health 20, no. 6: 5147. https://doi.org/10.3390/ijerph20065147
徐光祿,薛明,趙吉迪。 2023.「人工智慧意識與員工憂鬱之間的關聯:情緒耗竭的中介作用與感知組織支持的調節作用」國際環境研究與公共衛生雜誌20,第1期。 6:5147。

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