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1 – 10 of 103Pragati Agarwal, Sanjeev Swami and Sunita Kumari Malhotra
The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as…
Abstract
Purpose
The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations.
Design/methodology/approach
The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic.
Findings
The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper.
Research limitations/implications
Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis.
Practical implications
First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data.
Originality/value
As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic.
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Mojtaba Rezaei, Marco Pironti and Roberto Quaglia
This study aims to identify and assess the key ethical challenges associated with integrating artificial intelligence (AI) in knowledge-sharing (KS) practices and their…
Abstract
Purpose
This study aims to identify and assess the key ethical challenges associated with integrating artificial intelligence (AI) in knowledge-sharing (KS) practices and their implications for decision-making (DM) processes within organisations.
Design/methodology/approach
The study employs a mixed-methods approach, beginning with a comprehensive literature review to extract background information on AI and KS and to identify potential ethical challenges. Subsequently, a confirmatory factor analysis (CFA) is conducted using data collected from individuals employed in business settings to validate the challenges identified in the literature and assess their impact on DM processes.
Findings
The findings reveal that challenges related to privacy and data protection, bias and fairness and transparency and explainability are particularly significant in DM. Moreover, challenges related to accountability and responsibility and the impact of AI on employment also show relatively high coefficients, highlighting their importance in the DM process. In contrast, challenges such as intellectual property and ownership, algorithmic manipulation and global governance and regulation are found to be less central to the DM process.
Originality/value
This research contributes to the ongoing discourse on the ethical challenges of AI in knowledge management (KM) and DM within organisations. By providing insights and recommendations for researchers, managers and policymakers, the study emphasises the need for a holistic and collaborative approach to harness the benefits of AI technologies whilst mitigating their associated risks.
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Rexwhite Tega Enakrire and Bolaji David Oladokun
The purpose of this study is to investigate artificial intelligence (AI) as enabler of future library services, with consideration to how prepared are librarians in African…
Abstract
Purpose
The purpose of this study is to investigate artificial intelligence (AI) as enabler of future library services, with consideration to how prepared are librarians in African university libraries.
Design/methodology/approach
This study applied the interpretive content/document analysis of literature harvested from different databases of Scopus and Web of Science. AI could be used to perform daily routines in circulation, serial, reference and selective dissemination of information among others. It could also be applied to the provision of innovative services of recognition of library activities such as answering research quarries, cataloguing and classification of library materials and management of library system software of different databases within the library systems.
Findings
It could be deduced from the study that AI would continue to serve as a panacea to future library services irrespective of its geographical context. Due to the evolving nature of knowledge growth, AI having its roots in the field of engineering has been found useful to support future library services. The support accrued from library service delivery in the library profession has made librarians continue to interact with other intelligent machines that can demonstrate human behaviour even though they are not real human beings. The behaviour of machines and AI where human beings play a significant role has brought many renovations in the management of complex tasks of processing, communication, knowledge representation, decision making and suggestions, on potentials of diverse work operations.
Practical implications
The understanding that the present paper portrays in the context of future library services is that there is no way the AI could function without a human interaction perspective when drawing an analogy from computer science, information science and information systems fields of study.
Social implications
The interest of users across their background would be strengthen if AI advances transformed the handling complex tasks of processing, communication, knowledge representation, decision-making and giving suggestions, among other things. The possibilities of diverse work operations from empirical evidence of studies consulted in recent times gave the authors the impetus to consider AI as the enabler of future library services.
Originality/value
The increasing demands from library patrons have prompted librarians to adapt their methods of delivering services. These emerging technologies have also brought about shifts in approaches to teaching and learning. Consequently, the recent surge in digital technology-driven service innovations has ushered in a fresh paradigm for education and research. In response to these changes, librarians are actively seeking novel and innovative technologies to enhance user experiences within their libraries. They serve as catalysts for introducing modern and advanced technologies, consistently adapting to contemporary tools that enhance their offerings.
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Kamrul Hasan Bhuiyan, Selim Ahmed and Israt Jahan
The study investigates the consumer’s attitude to using artificial intelligence (AI) devices in hospitality service settings considering social influence, hedonic motivation…
Abstract
Purpose
The study investigates the consumer’s attitude to using artificial intelligence (AI) devices in hospitality service settings considering social influence, hedonic motivation, anthropomorphism, effort expectancy, performance expectancy and emotions.
Design/methodology/approach
This study employed a quantitative methodology to collect data from Bangladeshi consumers who utilized AI-enabled technologies in the hospitality sector. A total of 343 data were collected using a purposive sampling method. The SmartPLS 4.0 software was used to determine the constructs' internal consistency, reliability and validity. This study also applied the partial least squares structural equation modeling (PLS-SEM) to test the research model and hypotheses.
Findings
The finding shows that consumer attitude toward AI is influenced by social influence, hedonic motivation, anthropomorphism, performance and effort expectancy and emotions. Specifically, hedonic motivation, social influence and anthropomorphism affect performance and effort expectations, affecting consumer emotion. Moreover, emotions ultimately influenced the perceptions of hotel customers' willingness to use AI devices.
Practical implications
This study provides a practical understanding of issues when adopting more stringent AI-enabled devices in the hospitality sector. Managers, practitioners and decision-makers will get helpful information discussed in this article.
Originality/value
This study investigates the perceptions of guests' attitudes toward the use of AI devices in hospitality services. This study emphasizes the cultural context of the hospitality industry in Bangladesh, but its findings may be reflected in other areas and regions.
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Jonathan Passmore and David Tee
This study aimed to evaluate the potential of artificial intelligence (AI) as a tool for knowledge synthesis, the production of written content and the delivery of coaching…
Abstract
Purpose
This study aimed to evaluate the potential of artificial intelligence (AI) as a tool for knowledge synthesis, the production of written content and the delivery of coaching conversations.
Design/methodology/approach
The research employed the use of experts to evaluate the outputs from ChatGPT's AI tool in blind tests to review the accuracy and value of outcomes for written content and for coaching conversations.
Findings
The results from these tasks indicate that there is a significant gap between comparative search tools such as Google Scholar, specialist online discovery tools (EBSCO and PsycNet) and GPT-4's performance. GPT-4 lacks the accuracy and detail which can be found through other tools, although the material produced has strong face validity. It argues organisations, academic institutions and training providers should put in place policies regarding the use of such tools, and professional bodies should amend ethical codes of practice to reduce the risks of false claims being used in published work.
Originality/value
This is the first research paper to evaluate the current potential of generative AI tools for research, knowledge curation and coaching conversations.
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Sahil Narang and Rudra P. Pradhan
This study aims to examine the reaction of anchor investors (AIs) to pre-IPO earnings management (EM). The authors use the unique detailed bid data from the Indian anchor…
Abstract
Purpose
This study aims to examine the reaction of anchor investors (AIs) to pre-IPO earnings management (EM). The authors use the unique detailed bid data from the Indian anchor experiment. The authors also study the reputed AIs’ EM detection ability and pricing behavior in response to pre-IPO EM.
Design/methodology/approach
The authors use unique AI bid data for 169 Indian IPO firms. Utilizing the logistic regression and Tobit regression models with industry and year-fixed effects, the authors examine the relationship between various measures of AI participation and proxies of short-term and long-term discretionary accruals.
Findings
The authors document that pre-IPO EM is positively associated with the likelihood of anchor backing but negatively related to the likelihood of reputed anchor backing. The findings indicate that AIs are misled by pre-IPO EM, but reputed AIs are not. The authors also observe that reputed AIs, compared to the non-reputed, pay less than the upper band with increasing EM. The findings are robust to using various AI measures and EM proxies.
Practical implications
The findings have significant implications for regulators in the implementation of AI concept in non-anchor markets and better implementation of policies in existing anchor settings. Findings can also be relevant for non-institutional investors in the IPO domain.
Originality/value
This is one of the few studies on institutional investors' IPO bidding behavior in response to pre-IPO EM. However, this is the first study to analyze AIs' IPO bidding behavior in response to pre-IPO EM.
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Jinkyung Jenny Kim, Jungsun (Sunny) Kim, Kyu-Hyeon Joo and Jinsoo Hwang
The purpose of this study is to investigate the key predictors and outcomes of task–technology fit (TTF) of facial recognition payment systems with the moderating role of cultural…
Abstract
Purpose
The purpose of this study is to investigate the key predictors and outcomes of task–technology fit (TTF) of facial recognition payment systems with the moderating role of cultural differences in the restaurant industry.
Design/methodology/approach
The survey responses were collected from 336 South Korean and 336 US restaurant customers.
Findings
The results revealed that function significantly affected TTF in both groups. Unique to the Korean sample, emotion was found to be a significant determinant of TTF, whereas convenience and social influence were key predictors of TTF only for the US sample. TTF had significant and positive effects on the three dimensions of behavioral intentions in both groups. The result of multi-group analysis showed that cultural differences moderated the effect of convenience on TTF and the effect of emotion on TTF.
Originality/value
The authors provided recommendations for restaurant operators and technology companies seeking to improve customer TTF and acceptance of facial recognition payment systems for the first time.
研究目的
本研究旨在调查面部识别支付系统任务技术匹配(TTF)的关键前置因素和影响, 以文化差异为调节变量, 研究其在餐饮行业的应用。
研究方法
我们收集了来自336名韩国和336名美国餐厅顾客的调查回答。
研究发现
结果显示, 在两组中, 功能显著影响TTF。对于韩国样本来说, 情感被发现是TTF的重要决定因素, 而对于美国样本来说, 方便性和社会影响是TTF的关键预测因素。在两组中, TTF对行为意向的三个维度均产生了显著且积极的影响。多组分析结果显示, 文化差异在方便性对TTF的影响以及情感对TTF的影响中起到了调节作用。
研究创新
我们首次为寻求改善顾客TTF和接受面部识别支付系统的餐厅经营者和技术公司提供了建议。
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Hamad Mohamed Almheiri, Syed Zamberi Ahmad, Abdul Rahim Abu Bakar and Khalizani Khalid
This study aims to assess the effectiveness of a scale measuring artificial intelligence capabilities by using the resource-based theory. It seeks to examine the impact of these…
Abstract
Purpose
This study aims to assess the effectiveness of a scale measuring artificial intelligence capabilities by using the resource-based theory. It seeks to examine the impact of these capabilities on the organizational-level resources of dynamic capabilities and organizational creativity, ultimately influencing the overall performance of government organizations.
Design/methodology/approach
The calibration of artificial intelligence capabilities scale was conducted using a combination of qualitative and quantitative analysis tools. A set of 26 initial items was formed in the qualitative study. In the quantitative study, self-reported data obtained from 344 public managers was used for the purposes of refining and validating the scale. Hypothesis testing is carried out to examine the relationship between theoretical constructs for the purpose of nomological testing.
Findings
Results provide empirical evidence that the presence of artificial intelligence capabilities positively and significantly impacts dynamic capabilities, organizational creativity and performance. Dynamic capabilities also found to partially mediate artificial intelligence capabilities relationship with organizational creativity and performance, and organizational creativity partially mediates dynamic capabilities – organizational creativity link.
Practical implications
The application of artificial intelligence holds promise for improving decision-making and problem-solving processes, thereby increasing the perceived value of public service. This can be achieved through the implementation of regulatory frameworks that serve as a blueprint for enhancing value and performance.
Originality/value
There are a limited number of studies on artificial intelligence capabilities conducted in the government sector, and these studies often present conflicting and inconclusive findings. Moreover, these studies indicate literature has not adequately explored the significance of organizational-level complementarity resources in facilitating the development of unique capabilities within government organizations. This paper presents a framework that can be used by government organizations to assess their artificial intelligence capabilities-organizational performance relation, drawing on the resource-based theory.
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Wajde Baiod and Mostaq M. Hussain
This study aims to focus on the five most relevant and discursive emerging technologies in accounting (cloud computing, big data and data analytics, blockchain, artificial…
Abstract
Purpose
This study aims to focus on the five most relevant and discursive emerging technologies in accounting (cloud computing, big data and data analytics, blockchain, artificial intelligence (AI) and robotics process automation [RPA]). It investigates the adoption and use of these technologies based on data collected from accounting professionals in a technology-developed country – Canada, through a survey.
Design/methodology/approach
The study investigates the adoption and use of emerging technologies based on data collected from accounting professionals in a technology-developed country – Canada, through a survey. This study considers the said nature and characteristics of emerging technologies and proposes a model using the factors that have been found to be significant and most commonly investigated by existing prior technology-organization-environment (TOE)-related technology adoption studies. This survey applies the TOE framework and examines the influence of significant and most commonly known factors on Canadian firms’ intention to adopt the said emerging technologies.
Findings
Study results indicate that Canadian accounting professionals’ self-assessed knowledge (about these emerging technologies) is more theoretical than operational. Cloud computing is highly used by Canadian firms, while the use of other technologies, particularly blockchain and RPA, is reportedly low. However, firms’ intention about the future adoption of these technologies seems positive. Study results reveal that only the relative advantage and top management commitment are found to be significant considerations influencing the adoption intention.
Research limitations/implications
Study findings confirm some results presented in earlier studies but provide additional insights from a new perspective, that of accounting professionals in Canada. The first limitation relates to the respondents. Although accounting professionals provided valuable insights, their responses are personal views and do not necessarily represent the views of other professionals within the same firm or the official position of their accounting departments or firms. Therefore, the exclusion of diverse viewpoints from the same firm might have negatively impacted the results of this study. Second, this study sample is limited to Canada-based firms, which means that the study reflects only the situation in that country. Third, considering the research method and the limit on the number of questions the authors could ask, respondents were only asked to rate the impact of these five technologies on the accounting field and to clarify which technologies are used.
Practical implications
This study’s findings confirm that the organizational intention to adopt new technology is not primarily based on the characteristics of the technology. In the case of emerging technology adoption, the decision also depends upon other factors related to the internal organization. Furthermore, although this study found no support for the effect of environmental factors, it fills a gap in the literature by including the factor of vendor support, which has received little attention in prior information technology (IT)/ information system (IS) adoption research. Moreover, in contrast to most prior adoption studies, this study elaborates on accounting professionals’ experience and perceptions in investigating the organizational adoption and use of emerging technologies. Thus, the findings of this study are valuable, providing insights from a new perspective, that of professional accountants.
Social implications
The study findings may serve as a guide for researchers, practitioners, firms and other stakeholders, particularly technology providers, interested in learning about emerging technologies’ adoption and use in Canada and/or in a relevant context. Contrary to most prior adoption studies, this study elaborates on accounting professionals’ experience and perceptions in investigating the organizational adoption and use of emerging technologies. Thus, the findings of this study are valuable, providing insights from a new perspective, that of professional accountants.
Originality/value
The study provides insights into the said technologies’ actual adoption and improves the awareness of firms and stakeholders to the effect of some constructs that influence the adoption of these emerging technologies in accounting.
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Aleš Zebec and Mojca Indihar Štemberger
Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to…
Abstract
Purpose
Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.
Design/methodology/approach
The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.
Findings
The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.
Research limitations/implications
In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.
Practical implications
The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.
Originality/value
While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.
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