Search results

The prevalence of artificial intelligence (AI) has considerably affected management and society. This paper aims to explore its potential impact on hospitality industry employees, bringing enlightenment to both employees and managers.

Data were collected from a survey of 432 employees who worked in full-service hotels in China. Structural equation modeling (SEM) was used to analyze the data.

Results presented a positive relationship between AI awareness and job burnout. No significant direct relationship was found between AI awareness and career competencies. Organizational commitment mediated the relationship between AI awareness and career competencies, as well as the relationship between AI awareness and job burnout.

This study contributes to human resource management in the hospitality industry to theoretical and practical aspects. Theoretically, it enriched both career theory and fit theory. Practically, this study reminds managers to pay attention to the adverse effect of AI on human capital. It also enlightens the manager to think of the positive effects that AI may bring. Managers should provide proper support to overcome AI’s threat to human resources.

Practically, this study reminds managers to pay attention to the adverse effect of AI on human capital. It also enlightens the manager to think of the positive effects that AI may bring. Managers should provide proper support to overcome AI’s threat to human resources.

The study aims to analyze the impact of AI from a career perspective. It provided theoretical support and evidence for hotel managers for the effects of AI awareness on hotel employees. The study conveys a potential topic of concern that the hospitality industry may face in the future.

Artificial intelligence (AI) will be performing 52% of the tasks in companies by 2025. The increasing adoption of AI is generating technological turbulence in the business environment. Previous studies have also shown that employees are aware of the high risk of losing their jobs when being replaced by AI. The risk of employees engaging in opportunistic behaviors, such as knowledge hiding, is thus fairly high. Therefore, the aim of this paper is to analyze the mediating effect of employee’s AI awareness on the relationship between technological turbulence generated by AI and the three types of knowledge hiding: evasive hiding, playing dumb and rationalized hiding.

Structural equations by the partial least squares method were used to test the proposed research model.

The most interesting finding is that employee’s AI and robotics awareness fulfills almost all mediating functions in the relationship between technological turbulence generated by AI and the three types of knowledge hiding.

The results show that knowledge hiding in the digital age is first and foremost a strategy by employees to sabotage and induce failure in process automation, to reduce the risk of being replaced in the workplace by AI. This study indicates that employees are willing to hide knowledge in all possible ways when perception that AI is a threat to their job increases. In other words, technological turbulence generated by AI and employee’s AI awareness are the two great new triggers of knowledge hiding in the digital age.

This paper aims to identify ethical challenges of using artificial intelligence (AI)-based accounting systems for decision-making and discusses its findings based on Rest's four-component model of antecedents for ethical decision-making. This study derives implications for accounting and auditing scholars and practitioners.

This research is rooted in the hermeneutics tradition of interpretative accounting research, in which the reader and the texts engage in a form of dialogue. To substantiate this dialogue, the authors conduct a theoretically informed, narrative (semi-systematic) literature review spanning the years 2015–2020. This review's narrative is driven by the depicted contexts and the accounting/auditing practices found in selected articles are used as sample instead of the research or methods.

In the thematic coding of the selected papers the authors identify five major ethical challenges of AI-based decision-making in accounting: objectivity, privacy, transparency, accountability and trustworthiness. Using Rest's component model of antecedents for ethical decision-making as a stable framework for our structure, the authors critically discuss the challenges and their relevance for a future human–machine collaboration within varying agency between humans and AI.

This paper contributes to the literature on accounting as a subjectivising as well as mediating practice in a socio-material context. It does so by providing a solid base of arguments that AI alone, despite its enabling and mediating role in accounting, cannot make ethical accounting decisions because it lacks the necessary preconditions in terms of Rest's model of antecedents. What is more, as AI is bound to pre-set goals and subjected to human made conditions despite its autonomous learning and adaptive practices, it lacks true agency. As a consequence, accountability needs to be shared between humans and AI. The authors suggest that related governance as well as internal and external auditing processes need to be adapted in terms of skills and awareness to ensure an ethical AI-based decision-making.

Given that managers play a crucial role in developing and deploying AI for marketing financial services, this study was aimed at better understanding their awareness regarding AI and the challenges they are facing in providing the attendant technologies, as well as highlighting key stakeholders and their collaborative efforts in providing financial services.

Exploratory, inductive research design. The data was gathered through semi-structured interviews with 47 bank managers in both developed and developing countries, including the United Kingdom, Canada, Nigeria and Vietnam.

Managers are aware of the prospects of AI and are making efforts to address AI as a business need but find that there often exist certain challenges in accelerating AI adoption. The study also presents a conceptual framework of AI in relation to financial service marketing, which captures and highlights the interactions among the customers, banks and external stakeholders, as well as the regulators.

Banks must understand their business objectives, the available resources and the needs of their customers. Managers should keep the ethical implications of their working relationships in mind when selecting a team or collaborating with partners. In addition, managers should be trained and assisted in comprehending AI in relation to financial services, while the regulators must be involved in the development of AI for financial service marketing. Finally, it is critical to communicate the prospects for AI to consumers.

This study provides empirical insight into the opportunities, prospects and challenges pertaining to the use of AI in the area of financial service marketing. It also specifically calls into question certain preconceptions regarding AI and its role in financial services, the chatbots adopted for financial service delivery and the role of marketing managers in developing AI.

This study was launched because practitioners of Appreciative Inquiry (AI) instilled awareness for needed AI outcome research. Therefore, the goal of this research was to identify the salient AI processes and levers and the rate of AI success and failure. This study was specific to U.S. municipalities due to a researcher finding AI failure probability therein. In direct opposition, eight U.S. municipalities were identified from the literature as having utilized AI in 14 projects and all were successful even when resistance was present in three applications. A survey revealed 15 AI initiatives identified as successful even when resistance was present in eight, resulting in validation. This study utilized a mixed methods exploratory case study design, sequentially in the mix, consisting of a literature review and application of two unique instruments applied to three populations.

There is limited research on the behavior of different categories of space tourists as identified by different types of space tourism. To address this deficiency, the purpose of this study is to examine what factors make consumers participate in orbital and/or suborbital space tourism, along with three dimensions of motivation, constraint and artificial intelligence. To achieve this study’s goals, a comprehensive research model was developed that included three dimensions of intrinsic and extrinsic motivation, intrapersonal and interpersonal constraint and awareness of and trust in artificial intelligence, in comparing orbital and suborbital space tourism groups.

A questionnaire was carried out with respondents who wanted to participate in orbital (n = 332) and suborbital (n = 332) space tourism in the future. Partial least squares-structural equation modeling, fuzzy-set qualitative comparative analysis, multi-group analysis and deep learning were used to understand potential space tourist behavior.

Extrinsic motivation has the greatest positive impact on behavioral intention, followed by awareness of and trust in artificial intelligence, while intrapersonal constraint strongly negatively affects behavioral intention. Surprisingly, interpersonal constraint is insignificant by partial least squares-structural equation modeling but is still one of sufficient causal configurations by fuzzy-set qualitative comparative analysis. Interestingly, the two types of space tourism have very distinct characteristics.

This study created a comprehensive integrated research model with three dimensions of motivation, constraint and artificial intelligence, along with potential orbital and suborbital space tourist groups, to identify future consumer behavior. Importantly, this study used multi-analysis methods using four different approaches to better shed light on potential orbital and suborbital space tourists.

对不同类型太空旅游所识别的不同类别太空游客行为的研究有限。 为了解决这一缺陷, 这项工作研究了哪些因素使消费者参与轨道和/或亚轨道太空旅游, 以及动机、约束和人工智能三个维度。 为了实现研究目标, 在比较轨道和亚轨道太空旅游群体时, 开发了一个综合研究模型, 包括内在和外在动机、内在和人际约束以及对人工智能的认识和信任三个维度。

对希望在未来参与轨道 (n = 332) 和亚轨道 (n = 332) 太空旅游的受访者进行了问卷调查。 利用偏最小二乘法 (PLS)-结构方程模型 (SEM)、模糊集定性比较分析 (fsQCA)、多组分析和深度学习来了解潜在的太空游客行为。

外在动机对行为意图的积极影响最大, 其次是对人工智能的认识和信任, 而内在约束对行为意图有强烈的负面影响。 令人惊讶的是, 人际约束对于 PLS-SEM 来说是微不足道的, 但对于 fsQCA 来说仍然是充分的因果配置之一。 有趣的是, 这两类太空旅游具有非常鲜明的特点。

这项工作创建了一个全面的综合研究模型, 具有动机、约束和人工智能三个维度, 以及潜在的轨道和亚轨道太空旅游群体, 以确定未来的消费者行为。 重要的是, 这项研究采用了多种分析方法, 使用四种不同的方法来更好地揭示潜在的轨道和亚轨道太空游客。

existe una investigación limitada sobre el comportamiento de las diferentes categorías de turistas espaciales identificados por diferentes tipos de turismo espacial. Para abordar esta deficiencia, este trabajo examina qué factores hacen que los consumidores participen en el turismo espacial orbital y/o suborbital, junto con tres dimensiones de motivación, restricción e inteligencia artificial. Para lograr los objetivos del estudio, se desarrolló un modelo de investigación integral que incluía tres dimensiones de motivación intrínseca y extrínseca, restricción intrapersonal e interpersonal, y conocimiento y confianza en la inteligencia artificial, al comparar grupos de turismo espacial orbital y suborbital.

se realizó un cuestionario con los encuestados que querían participar en el turismo espacial orbital (n = 332) y suborbital (n = 332) en el futuro. Se utilizaron modelos de ecuaciones estructurales (SEM) de mínimos cuadrados parciales (PLS), análisis comparativo cualitativo de conjuntos borrosos (fsQCA), análisis multigrupo y aprendizaje profundo para comprender el comportamiento potencial del turista espacial.

la motivación extrínseca tiene el mayor impacto positivo en la intención de comportamiento, seguida de la conciencia y la confianza en la inteligencia artificial, mientras que la restricción intrapersonal afecta negativamente la intención de comportamiento. Sorprendentemente, la restricción interpersonal es insignificante por PLS-SEM, pero sigue siendo una de las configuraciones causales suficientes por fsQCA. Curiosamente, los dos tipos de turismo espacial tienen características muy distintas.

este trabajo creó un modelo de investigación integral integral con tres dimensiones de motivación, restricción e inteligencia artificial, junto con posibles grupos de turistas espaciales orbitales y suborbitales para identificar el comportamiento futuro del consumidor. Es importante destacar que este estudio empleó métodos de análisis múltiple utilizando cuatro enfoques diferentes para arrojar mejor luz sobre los posibles turistas espaciales orbitales y suborbitales.

The purpose of this study is to provide recommendations for policy framework on artificial intelligence (AI) in India.

Studies have been conducted through focus group discussion and the other sources such as different company websites using AI, Indian Government strategy reports on AI, literature studies, different policies implemented on AI in different locations and other relevant documents. After those studies, a charter of recommendation has been provided. This will help the authority to frame the AI policy for India.

This study highlights that “National Strategy for AI” for India needs improvement to provide comprehensive inputs for framing policy on AI. This study also implies that focus is to be given on security, privacy issues including issues of governance.

AI-related technology has immense potential toward the development of organizations. This study implies the necessity of framing a comprehensive policy on AI for India. If there is a comprehensive policy on AI for India, the Indian industries will derive many benefits.

This study provides inputs on how the industries of India can be benefitted with the help of AI and how R&D can develop the AI activities to harness maximum benefits from this innovative technology.

AI-related policy will have appreciable influence on the society in terms of human–device interactions and communications. The policy framework on AI for India is expected to project far-reaching effects toward deriving benefits to the society.

This paper has taken a holistic and unique attempt to provide inputs to the policymakers for framing a comprehensive and meaningful policy on AI for India.

Artificial intelligence (AI) is used by banking services primarily to automate systems; however, this ecosystem does not work in emerging markets because human intervention is needed, and there are concerns related to infrastructure. There is plenty of research on AI-mediated banking services, but the existing discussions are cumbersome, and studies on AI's service features in banking for emerging markets are limited. Furthermore, the ongoing discussions are centred on developed markets where automation in banking services is noteworthy and accepted. Through this paper, the authors emphasise the relevance of AI mediation in emerging markets and the possible role of strategising AI in banking services for personalised experiences.

The authors' article followed an exploratory, inductive approach through in-depth interviews and thematic analysis. In total, 36 financial experts were interviewed, and the relevant perspectives were analysed to develop the research process and framework for a personalised banking experience.

The authors' paper introduced five key themes and presented those themes accordingly. The first theme details the importance of AI-mediated banking and the skills necessary for operational capacity. The second theme is on the relevance of AI-mediated banking awareness amongst users. The third is about channelling the importance of AI-driven interfaces through managers and employees. Fourth, the authors emphasised the relevance of human intervention due to users' demographic patterns. The fifth theme led to a discussion on personalised AI-mediated banking services.

The authors recommend that managers understand the relevance of quality service amongst users. The authors' paper discusses the relevance of AI and human intervention in banking services; however, the process for seamless, personalised banking experiences is not provided. Thus, this paper encourages managers to build a banking ecosystem that delivers a seamless banking experience through AI.

The authors' paper highlights the importance of human intervention in AI-driven banking by introducing personalised service experience elements and highlighting the role of customer experience in AI-driven banking services in emerging markets.

The purpose of this research paper is to evaluate and estimate the cybersecurity maturity and awareness risk for workforce management in railway transportation by using Railway-Cybersecurity Capability Maturity Model (R-C2M2) and Information Security Awareness Capability Model (ISACM), respectively.

This research uses a case study strategy, so primary data comprise the majority of data collected. These data were collected through interviews and questionnaires. The secondary data were collected from the literature, technical reports and standards.

The results show that there is a gap in cybersecurity awareness within the workforce and there is a need to improve this gap. This paper provides some of the recommendations and literature to enhance cybersecurity workforce culture within railway organizations.

In this paper, the authors have demonstrated that cybersecurity awareness has positive impact on the overall dependability of the railway system.

This paper describes the importance of cybersecurity awareness and training in building more cyber resiliency across the operation and maintenance of railway.

The rise of artificial intelligence (AI) and differing attitudes towards its adoption in the building and environment (B&E) industry has an impact upon whether companies can meet changing demand and remain relevant and competitive. The emergence of Industry 4.0 technologies, coupled with the repercussions of COVID-19, increases the urgency and opportunities offered that companies must react to, as disruptive technologies impact how project management (PM) professionals work and necessitate acquisition of new skills. This paper attempts to identify the drivers of and barriers to, as well as the general perception and receptiveness of local PM professionals towards, AI adoption in PM and thereby propose potential strategies and recommendations to drive AI adoption in PM.

This study employs both quantitative and qualitative approaches to examine the findings gathered. A survey questionnaire was used as the primary method of gathering quantitative data from 60 local PM professionals. Statistical tests were performed to analyse the data. To substantiate and validate the findings, in-depth interviews with several experienced industry professionals were performed.

It is found that top drivers include support from top management and leadership, organisational readiness and the need for greater work productivity and efficiency. Top barriers were found to be the high cost of AI implementation and maintenance and the lack of top-down support and skilled employees trained in AI. These findings could be attributed to the present state of AI technologies being new and considerably underutilised in the industry. Hence, substantial top-down support with the right availability of resources and readiness, both in terms of cost and skilled employees, is paramount to kick-start AI implementation in PM.

Little research has been done on the use of AI in PM locally. AI's potential to improve the productivity and efficiency of PM processes in the B&E industry cannot be overlooked. An understanding of the drivers of, barriers to and attitudes towards AI adoption can facilitate more intentional and directed oversight of AI's strategic roll-out at both the governmental and corporate levels and thus mitigate potential challenges that may hinder the implementation process in the future.