A study of employee acceptance of artificial intelligence technology

Choi, Youngkeun

doi:10.1108/ejmbe-06-2020-0158

Cited by 22 publications

(10 citation statements)

References 39 publications

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“…Similarly, data privacy concern was another negative factor in adopting AI [17]. Choi discovered the moderating effect of privacy concern between role clarity and employees' willingness to AI adoption, which also illustrated the negative effect of concern on behavior intention [29].…”

Section: Negative Impact On Behaviormentioning

confidence: 87%

Co-working with AI is a Double-sword in Technostress? An Integrative Review of Human-AI Collaboration from a Holistic Process of Technostress

Xia

2023

SHS Web Conf.

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An accumulating body of research has demonstrated that human-AI (Artificial Intelligence) collaboration is an indistinguishable future of work. The study of employee-perspective, affect-related responses, and job-related consequences from the adoption and use of AI is gaining attraction in the field of business and management. Some research has examined the technostress creators or negative responses related to human-AI collaboration, however, a holistic process of technostress that considers both positive and negative impacts has not been adopted. This study integrates the findings to examine the relationship between technostress and human-AI collaboration, based on the technostress creators (challenge technostressors and hindrance technostressors), positive and negative psychological responses, and outcomes in work-related well-being and intention to use AI. Future research opportunities on technostress generated by human-AI collaboration are discussed alongside implications to theory and management practices.

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Section: Negative Impact On Behaviormentioning

confidence: 87%

Co-working with AI is a Double-sword in Technostress? An Integrative Review of Human-AI Collaboration from a Holistic Process of Technostress

Xia

2023

SHS Web Conf.

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“…First, the acceptance of employees to use these models is important to consider. According to current surveys, the acceptance of employees to use AI applications depends on the clarity of user and AI roles as well as trust in AI systems (Choi, 2021). However, to the best of our knowledge, no studies have yet been conducted investigating employee acceptance of AI-based forecasts by accounting professionals or examining their role and range of tasks within the forecasting cycle.…”

Section: Discussion and Open Researchmentioning

confidence: 99%

Forecasting in financial accounting with artificial intelligence – A systematic literature review and future research agenda

Kureljusic

Karger

2023

JAAR

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PurposeAccounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.Design/methodology/approachThe authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.FindingsThe authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.Research limitations/implicationsOwing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.Practical implicationsGiven the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.Originality/valueTo the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

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“…To reach the objectives of the investigation, it is proposed that the primary sociodemographic variables be included in the study in addition to those previously mentioned. The scientific literature reveals a strong association between socio-demographic characteristics and the adoption of different technologies (for example: Guttentag and Smith, 2020;Molinillo et al, 2020;Choi, 2021). The socio-demographic factors of the respondents, such as gender, age, level of education, size of household and size of the municipality are influencing factors in the intention to adopt and the intention to continue using a technology.…”

Section: Literature Review 21 Socio-demographic Characteristics As Se...mentioning

confidence: 99%

The consumer intention to use e-commerce applications in the post-pandemic era: a predictive approach study using a CHAID tree-based algorithm

Higueras-Castillo,

Alves,

Liébana-Cabanillas

et al. 2023

EJMBE

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PurposeThis study proposes a hierarchic segmentation that develops a tree-based classification model and classifies the cases into groups. This allows for the definition of e-commerce user profiles for each of the groups. Additionally, it facilitates the development of actions to improve the adoption of the online channel that is in such high demand in the current pandemic COVID-19 context.Design/methodology/approachRegarding the created segments, two extreme segments stand out due to their marked differences and high volume. Segment 3 with 23% of the sample is the group with the most predisposition to use the online channel and is characterised by a high level of trust, more habitual use in comparison with other groups and the belief that its use implies high performance, which indicates they believe it to be useful, quick and helpful for more an effective shopping experience. The other extreme is found in segment 7. This group makes up 17.7% of the total and is the most reluctant to use the online channel. These users are characterised by the complete opposite: they have a low level of trust in this channel. However, the effort expectancy is low, i.e. they consider that the adoption of the online channel does not involve many difficulties in its learning and use. Nevertheless, they use it less regularly than the others.FindingsBased on the conclusions reached in this study, in the current pandemic context in which consumer demand for online shopping channels for all types of products is on the rise, it is recommended that companies focus on the following aspects. It is essential to build trust with the user and show them the real benefits of e-commerce, how it would improve their life and why they should use it. Additionally, it is vital that the user perceives it as an easy procedure that does not require a significant learning curve. Other fundamental aspects would be to reduce any uncertainty the user might have about the online shopping process, to make it as easy as possible, and to design a simple, intuitive and user-friendly interface. It is also recommendable to manage data usage efficiently. To do so, the authors recommend asking the user for the least amount of information possible, offering a data protection policy and assuring them that their information will not be misused nor shared with third parties. All of this provides a series of facilities to modify the online shopping habits of users. Research limitations/implicationsAs in most of the research, this study presents a series of limitations that should be debated and that could open future lines of investigation. Firstly, regarding the sample used that was limited to two neighbouring countries with similar profiles a priori; it would be necessary to compare their possible cultural differences according to Hofstede's dimensions as well as increase the number of European countries being analysed to reach a more generalised conclusions. Secondly, the variables used are a combination of those derived from the UTAUT2 model and others suggested in the literature as decisive in technology adoption by users, in this sense other theories and variables could be incorporated to complete a more holistic model.Practical implicationsThis work contributes in a general way to (1) analysing the intention to use e-commerce platforms from a set of antecedents previously defined by their importance, after a period of economic and social restrictions derived from the pandemic; (2) determination of customer segments from the classification made by the CHAID analysis; (3) characterisation of the previously defined segments through the successive divisions that were proposed in the analysis carried out.Social implicationsOther fundamental aspects would be to reduce any uncertainty the user might have about the online shopping process to make it as easy as possible, and to design a simple, intuitive, and user-friendly interface. It is also recommended to manage data usage efficiently. To do so, the authors recommend asking the user for the least amount of information possible, offering a data protection policy, and assuring them that their information will not be misused or shared with third parties.Originality/valueThe results obtained have allowed us to establish predictive and explanatory models of the behaviour of the segments and profiles created, which will help companies to improve their relationships with online customers in the coming years.

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A study of employee acceptance of artificial intelligence technology

Cited by 22 publications

References 39 publications

Co-working with AI is a Double-sword in Technostress? An Integrative Review of Human-AI Collaboration from a Holistic Process of Technostress

Co-working with AI is a Double-sword in Technostress? An Integrative Review of Human-AI Collaboration from a Holistic Process of Technostress

Forecasting in financial accounting with artificial intelligence – A systematic literature review and future research agenda

The consumer intention to use e-commerce applications in the post-pandemic era: a predictive approach study using a CHAID tree-based algorithm

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