The Technology Acceptance Model and AI in HRM for the Beauty Industry

TAM is a framework for understanding technology adoption and use Perceived utility and ease of use are key determinants of technology adoption TAM can be applied to understand AI adoption in HRM in the beauty industry

Technology Acceptance Model in Related to AI in HRM of Beauty Industry
According to Granić and Marangunić (2019), a popular theoretical framework for
comprehending how people adopt and use new technology is the Technology Acceptance Model
(TAM). It was initially put up by Fred Davis in 1986, and other scholars have subsequently
improved and expanded upon it. The fundamental tenet of TAM is that perceived utility (PU)
and perceived ease of use (EU) are the main determinants of people's desire to utilise a
technology (PEOU). Al-Emran et al. (2018), states that perceived usefulness relates to how much
a technology is thought to be beneficial for carrying out a certain activity, whereas perceived
ease of use refers to how much a technology is thought to be simple to learn and use.
TAM claims that these two elements have a direct impact on an individual's attitude towards
adopting a technology, which in turn has an impact on their desire to utilise it. Al-Emran et al.
(2018), mentions that subjective norms, or the impression of societal pressure to use or not use a
technology, can also have an impact on one's attitude towards utilising it. Moreover, perceptions
of behavioural control, or perceived technological competence, might have an indirect impact on
intention through their effects on attitude. TAM has been widely employed in studies linked to
the adoption and usage of many technologies, including information systems, e-commerce,
mobile apps, and social media. As per Granić and Marangunić (2019), it has been discovered to
be an effective framework for anticipating and comprehending user behaviour, and it has assisted
in directing the creation of better user-friendly technology.
As stated by Singh et al. (2020), to comprehend how workers in the beauty industry may see and
accept the use of artificial intelligence (AI) in human resource management, the Technological
Acceptance Model (TAM) might be implemented (HRM). Some ways that TAM may be used in
the context of AI in HRM for the beauty business include the following:
 Perceived utility: As opined by Hmoud and Várallyai (2020), a key component in
determining whether or not employees would adopt AI in HRM is their impression of its
utility. Employees are more inclined to adopt AI if they believe it can improve HRM
operations in terms of accuracy, efficiency, and personalization.
 Perceived ease of use: Employee acceptability of AI in HRM will also be influenced by
how simple it is for them to utilise. Employee acceptance of AI-powered HRM solutions
is influenced by how user- and learning-friendly they are.

 Attitude towards using AI in HRM: Employee views about the usage of artificial
intelligence in human resources management will affect their desire to adopt the
technology. As opined by Hmoud and Várallyai (2020), Employees are more likely to
embrace and apply AI in HRM if they have a good attitude towards it.
 Subjective norms: As per Singh et al. (2020), employee adoption of AI in HRM will also
be influenced by the perceived societal pressure to utilise it or not. Employees are more
inclined to adopt and apply AI in HRM if they believe that their coworkers or superiors
are doing so.
 Perceived behavioral control: The acceptability of AI in HRM will also depend on how
the employees see their ability to manage their behaviour. Employees are more inclined
to adopt and apply AI in HRM if they believe they have the requisite resources and
abilities.
As opined by Hmoud and Várallyai (2020), companies may build and deploy AI-powered HRM
solutions that are more likely to be accepted and embraced by employees by understanding how
TAM relates to AI in HRM for the beauty industry. This may lead to enhanced speed, accuracy,
and personalisation in HRM procedures, eventually leading to higher employee engagement and
satisfaction.
Challenges of AI in HRM for Competitive Advantage of Beauty Industry
Wirtz et al. (2019), states that the beauty industry is a very competitive one, with organisations
constantly looking for ways to gain a competitive advantage. Businesses may do this by adding
AI into their human resource management practises. AI has the potential to improve human
resource management approaches by increasing efficiency and effectiveness, providing insights
into employee performance, and improving employee experience. However, as per Hossin et al.
(2021), there are a number of issues that must be addressed before AI may be successfully
applied in HRM.
Data quality and privacy
According to Budhwar etal. (2022), organizations always strive to surpass one another in the
highly competitive beauty sector. One of the hardest components of using AI in HRM is making
sure data protection and integrity. The data used to train AI models must be reliable,
comprehensive, and objective, according to human resources departments. Also, Budhwar etal.

(2022), states that it's important to look into data privacy laws like the GDPR and CCPA to make
sure that employee data is handled legally and morally. HR departments need to carefully
consider the data they gather and how they utilise it in order to guarantee compliance with ethical
and statutory norms.
Change Resistance
Dwivedi et al. (2021), a version to change is another hurdle to AI adoption in HRM. When
adopting new technology, such as AI, change aversion is common. Employee concerns must be
addressed, and appropriate training and assistance must be offered to ensure that employees are
comfortable with the new technology. This includes not just technical training, but also
education on the benefits of AI and how it may improve human resource management practises.
As per Hossin et al. (2021), HR departments may help guarantee that AI is used effectively in
HRM by addressing employee concerns and providing training and assistance.
Prejudice and bias
Bias and prejudice are another issue that must be addressed when incorporating AI in HRM.
Wirtz et al. (2019), states that if the training data used to construct AI algorithms is biassed, the
algorithms themselves may be distorted. As a result, discriminating behaviours such as biassed
hiring decisions or biassed performance evaluations may arise. HR departments must ensure that
AI models are free of bias and are inspected on a regular basis to identify and correct any biases
that may arise. As a result, Wirtz et al. (2019), mentions that HR departments must carefully
evaluate the data they use to train AI models and take steps to eliminate data biases.
Interoperability with Current Systems
For implementing AI in HRM, Budhwar et al. (2022), says interoperability with current systems
is a challenge that must be overcome. HR departments may face technical challenges when
integrating AI technologies with existing HR systems, such as ensuring that the AI system can
connect with the existing HR systems. They may also face organisational challenges, such as
ensuring that the AI system is consistent with the culture and values of the firm. To ensure a
successful deployment, Hossin et al. (2021), suggests human resources departments must
extensively investigate the technological and organisational difficulties associated with
integrating AI and take steps to mitigate them.

Balancing Technology and the Human Touch
The need to reconcile the use of technology with the human touch is another difficulty of
applying AI in HRM for competitive advantage in the beauty business. According to Dwivedi et
al. (2021), employee engagement and morale may suffer as a result, which may have an adverse
effect on output and the bottom line. To promote a great employee experience, HR departments
need to strike a balance between employing AI to simplify HRM procedures and keeping in
contact with employees on a human level. This may be accomplished through offering frequent
contact and feedback, giving employees the chance to grow professionally, and expressing
gratitude for their efforts and contributions.

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