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Cited by 33AI and HR TechnologiesEmployer Branding and e-HRMTechnology Adoption and User Behaviour

Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector

Open access
Goswami, Manisha, Jain, Supriya, Alam, Tabish, Deifalla, Ahmed Farouk, Ragab, Adham E., Khargotra, Rohit·2023·Frontiers in Pharmacology
DOI 10.3389/fphar.2023.1215706PMC PMC10682089
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Abstract

Purpose: The aim of this research is to investigate the factors that facilitate the adoption of artificial intelligence (AI) in order to establish effective human resource management (HRM) practices within the Indian pharmaceutical sector. Design/methodology/approach: A model explaining the antecedents of AI adoption for building effective HRM practices in the Indian pharmaceutical sector is proposed in this study. The proposed model is based on task-technology fit theory. To test the model, a two-step procedure, known as partial least squares structural equational modeling (PLS-SEM), was used. To collect data, 160 HRM employees from pharmacy firms from pan India were approached. Only senior and specialized HRM positions were sought. Findings: An examination of the relevant literature reveals factors such as how prepared an organization is, how people perceive the benefits, and how technological readiness influences AI adoption. As a result, HR systems may become more efficient. The PLS-SEM data support all the mediation hypothesized by proving both full and partial mediation, demonstrating the accuracy of the proposed model. Originality: There has been little prior research on the topic; this study adds a great deal to our understanding of what motivates human resource departments to adopt AI in the pharmaceutical companies of India. Furthermore, AI-related recommendations are made available to HRM based on the results of a statistical analysis.

AI snapshot

Written from the paper’s full text.

TL;DR

The study proposes and tests a task-technology fit model of AI adoption for effective HRM in the Indian pharmaceutical sector. Using PLS-SEM on 88 usable HRM responses, it finds AI adoption strongly predicts effective HRM and mediates all five hypothesized antecedent paths.

Key findings

  • AI adoption had a strong positive relationship with effective HRM (b = 0.396; t = 3.71; p = 0.00).
  • Direct antecedents of AI adoption were supported for organizational culture (b = 0.397; t = 3.24; p = 0.00) and perceived benefits (b = 0.214; t = 2.01; p = 0.04), but not for competitive pressure (b = 0.084; p = 0.47), organizational preparedness (b = 0.118; p = 0.25), or technological readiness (b = 0.117; p = 0.44).
  • Direct effects on effective HRM were supported for organizational preparedness (b = 0.258; p = 0.01), technological readiness (b = 0.391; p = 0.00), competitive pressure negatively (b = −0.201; p = 0.03), and AI adoption (b = 0.396; p = 0.00), but not for organizational culture (b = −0.039; p = 0.73) or perceived benefits (b = 0.146; p = 0.13).
  • AI adoption mediated all five indirect paths: perceived benefits → AI → effective HRM (b = 0.15; p = 0.00; full mediation), competitive pressure → AI → effective HRM (b = 0.12; p = 0.02; partial), technological readiness → AI → effective HRM (b = 0.10; p = 0.00; partial), organizational culture → AI → effective HRM (b = 0.16; p = 0.01; full), and organizational preparedness → AI → effective HRM (b = 0.15; p = 0.00; partial). R² was 0.66 for AI adoption and 0.73 for effective HRM.

Methods

  • Descriptive survey design using task-technology fit theory; constructs included organizational preparedness, perceived benefits, technological readiness, competitive pressure, organizational culture, AI adoption, and effective HRM, measured with adapted 5-point Likert scales; content/face validity was checked by two pharmaceutical-sector experts and a pilot study.
  • Respondents were senior/specialized HRM employees in Indian pharmaceutical firms with at least 5 years of sector experience, recruited by convenience sampling; 250 questionnaires were circulated, 150 responses were received (60% response rate), and after eliminating incomplete/incorrect responses, 88 responses were analyzed; respondents were 80 male and 8 female, aged 30–39, from cities including Vadodara, Ahmedabad, Baddi, Sikkim, Kolkata, Visakhapatnam, Hyderabad, Bangalore, Chennai, Mumbai, and Pune; data collection ran November 2022–January 2023.
  • Analysis used PLS-SEM in SmartPLS: measurement model assessment (indicator loadings >0.70; Cronbach’s alpha 0.87–0.93; composite reliability 0.91–0.94; AVE 0.67–0.77; Fornell–Larcker and HTMT discriminant validity), structural model assessment with VIF <5, bootstrapping with 5,000 samples at 95% confidence level, and mediation analysis.

Limitations

  • Paper-stated: data came only from major cities; only the pharmaceutical industry was considered; AI was treated as a high-tech tool; findings are limited to India and not generalizable to other countries; only 160 HR managers from Indian pharmaceutical companies were involved; future research should examine other fields, other cultures/nations, and develop India-specific scales.
  • Evident from the text: the final analytic sample is small (88) and gender-imbalanced (80 male, 8 female); convenience sampling limits representativeness; the cross-sectional design cannot establish causality; and the sample counts are inconsistent across the abstract, methodology, and limitations (160 approached vs. 250 circulated, 150 returned, 88 analyzed).

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3389/fphar.2023.1215706

Cites

Statistics

Nothing to recompute.

Claims the paper makes

0 of 103 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    AI had a strong positive relationship with effective HRM (H1) (b = 0.40; t = 3.71; p = 0.00).

    “artificial intelligence had a strong positive relationship with effective HRM (H1) (b = 0.40; t = 3.71; p = 0.00); hence, H1 is supported.”
    Quote in the paperSubstantial claim
  • Findingown data

    Perceived benefits fail to establish a relationship with effective HRM (H9) (b = 0.15; t = 1.51; p = 0.13).

    “perceived benefits fail to establish a relationship with effective HRM in the study (H9) (b = 0.15; t = 1.51; p = 0.13).”
    Quote in the paperNot a substantial claim
  • Findingown data

    The relationship between competitive pressure and AI could not be established (H2) (b = 0.08; t = 0.72; p = 0.47).

    “the relationship between competitive pressure and artificial intelligence (H2) could not be established in the study (b = 0.08; t = 0.72; p = 0.47); hence, it was found to be insignificant.”
    Quote in the paperNot a substantial claim
  • Findingown data

    Perceived benefit has a strong positive relation with AI (H8) (b = 0.21; t = 2.01; p = 0.04).

    “the perceived benefit has a strong positive relation with artificial intelligence (H8) (b = 0.21; t = 2.01; p = 0.04); thus, H7 and H8 are supported.”
    Quote in the paperSubstantial claim
  • Findingown data

    Competitive pressure has a significant relationship with effective HRM through the mediation of AI adoption (H13) (b = 0.12; t = 2.24; p = 0.02).

    “CP ->AI ->EH competitive pressure has a significant relationship with effective HRM through the mediation of AI adoption (H 13 ) (b = 0.12; t = 2.24; p = 0.02)”
    Quote in the paperSubstantial claim
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    Findingown data

    The PLS-SEM data support all the mediation relationships hypothesized in the study, with both full and partial mediation, demonstrating the accuracy of the proposed model.

    “The PLS-SEM data support all the mediation hypothesized by proving both full and partial mediation, demonstrating the accuracy of the proposed model.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The respondents fall in the age bracket of 30-39 years old.

    “The respondents fall in the age bracket of 30–39 years old”
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    Responses were received from all parts of the country, namely, Vadodara, Ahmedabad, Baddi, Sikkim, Kolkata, Visakhapatnam, Hyderabad, Bangalore, Chennai, Mumbai, and Pune.

    “have received responses from all parts of the country, namely, Vadodara, Ahmedabad, Baddi, Sikkim, Kolkata, Visakhapatnam, Hyderabad, Bangalore, Chennai, Mumbai, and Pune.”
    Quote in the paperNot a substantial claim

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    Findingcitation

    Organizational preparedness has a significant indirect relationship with effective HRM through the mediation of AI adoption (H16) (b = 0.15; t = 3.98; p = 0.00).

    “OP ->AI ->EH AI adoption on the relationship between organizational pressure and effective HRM (H 16 ) (Hmoud, 2021) (b = 0.15; t = 3.98; p = 0.00)”
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    Findingown data

    Of the 88 respondents, 8 were female and 80 were male.

    “In the demographic profile of the 88 respondents of the pharmaceutical sector, out of which only eight were female individuals and the remaining 80 were male individuals”
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    Findingown data

    R2 is 0.66 for AI adoption, indicating that five latent variables significantly explain 66% of variance in AI adoption.

    “R 2 is 0.66 for artificial intelligence adoption and 0.73 for the effective HRM endogenous latent variable, indicating that the other five latent variables (like perceived benefits, competitive pressure, technological readiness, organizational culture, and organizational pressure) significantly explain 66% (R 2 = 0.66) of variance in artificial intelligence adoption.”
    Quote in the paperSubstantial claim

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    Findingown data

    Organizational culture and perceived benefits aid in the adoption of AI by pharmaceutical companies.

    “According to the research, organizational culture and how people perceive the benefits aid in the adoption of AI by pharmaceutical companies.”
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    Premisecitation

    In the context of AI-powered HRM practices, the technology should be aligned with the HR tasks and functions required in the Indian pharmaceutical sector.

    “Furthermore, in the context of AI-powered HRM practices, the technology should be aligned with the HR tasks and functions required in the Indian pharmaceutical sector (Pandey, 2020; Mahmoud, 2021; Prikshat et al., 2023).”
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    Premisecitation

    AI-powered HRM practices can enhance the efficiency of HR processes, improve the quality of talent acquisition and retention, and contribute to better decision-making.

    “AI-powered HRM practices can enhance the efficiency of HR processes, improve the quality of talent acquisition and retention, and contribute to better decision-making (Muduli and Trivedi, 2020; Wheeler and Buckley, 2021).”
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    The Indian pharmaceutical industry operates under a complex and evolving regulatory environment.

    “However, the Indian pharmaceutical industry operates under a complex and evolving regulatory environment (Imran et al., 2013).”
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    Physicians and healthcare professionals might be hesitant to trust AI-driven diagnoses or treatment recommendations, leading to slow adoption and resistance to change.

    “Physicians and healthcare professionals might be hesitant to trust AI-driven diagnoses or treatment recommendations, leading to slow adoption and resistance to change (Yokoi et al., 2021).”
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    Interpretationreasoning

    TTF is a more suitable approach when the goal is to understand the role of technology in supporting specific tasks or processes in an organization.

    “Therefore, TTF is a more suitable approach when the goal is to understand the role of technology in supporting specific tasks or processes in an organization.”
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    Premisecitation

    TTF provides a more nuanced view of how technology can be used to enhance task performance.

    “It provides a more nuanced view of how technology can be used to enhance task performance and can be a useful tool for organizations to ensure that the technology they adopt is a good fit for the tasks performed by their employees (Chang et al., 2023).”
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    The technology acceptance model proposes that perceived usefulness and perceived ease of use are the main determinants of user acceptance of technology.

    “The technology acceptance model (TAM) proposes that perceived usefulness and the perceived ease of use are the main determinants of the user acceptance of technology (Davis, 1985).”
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    The TTF model has been widely researched to examine the adoption of technology in areas like healthcare, big data, banking and finance, and training management systems.

    “The TTF model has been widely researched to examine the adoption of technology in areas like healthcare, big data, banking and finance, and training management systems (Tam and Oliveira, 2016; Sharif et al., 2019).”
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    TTF theory provides a structured framework for researchers and practitioners to assess users' attitudes and behaviors toward technology adoption.

    “Overall, the TTF theory provides a structured framework for researchers and practitioners to assess users’ attitudes and behaviors toward technology adoption, helping organizations make informed decisions and strategies to enhance technology acceptance and successful implementation (Yen et al., 2010).”
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    Numerous studies are conducted on TOE, assessing the factors in successful AI implementation at the individual level in various domains.

    “Furthermore, numerous studies are conducted on TOE, assessing the factors in successful AI implementation at the individual level in various domains (Alsheibani et al., 2020; Chen et al., 2021; Oyetunde et al., 2022).”
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    TTF has been widely applied and tested in various contexts and industries, including the adoption of new technologies in business, healthcare, and education.

    “The task-technology fit (TTF) theory has been widely applied and tested in various contexts and industries, including the adoption of new technologies in business, healthcare, and education, and many other domains (Faqih and Jaradat, 2021).”
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    Artificial intelligence will have an impact on human resources.

    “Artificial intelligence will have an impact on human resources (Jia et al., 2018; Johansson and Herranen, 2019; Chowdhury et al., 2023).”
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    TTF focuses on the fit between the characteristics of the technology and the requirements of the task or job.

    “In other words, TTF focuses on the fit between the characteristics of the technology and the requirements of the task or job (Dishaw and Strong, 1999).”
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    TTF has been useful in predicting user acceptance and adoption of technologies and in identifying potential barriers to adoption.

    “It has been useful in predicting user acceptance and adoption of technologies and in identifying potential barriers to adoption that can be addressed to facilitate a successful implementation (Abdekhoda et al., 2014; Taherdoost, 2022).”
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    Effective implementation of AI in the pharmaceutical industry relies on access to vast amounts of high-quality patient data.

    “Effective implementation of AI in the pharmaceutical industry relies on access to vast amounts of high-quality patient data (Blanco-González et al., 2023).”
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    The Indian pharmaceutical sector faces challenges such as a lack of awareness about AI technology, resistance from employees, and the cost of adoption.

    “Moreover, this sector faces several challenges, such as a lack of awareness about AI technology, resistance from employees, and the cost of adoption (Li et al., 2023).”
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    The task-technology fit theory suggests that the adoption of technology is influenced by individual and organizational factors.

    “The task-technology fit theory also suggests that the adoption of technology is influenced by other factors, such as individual and organizational factors (Goodhue and Thompson, 1995; Lin, 2014; Alyoussef, 2023).”
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    HR managers are less likely to adopt AI if they are not sufficiently informed about its benefits and effectiveness.

    “HR managers are less likely to adopt AI if they are not sufficiently informed about its benefits and effectiveness (Guenole and Feinzig, 2018).”
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    An organization's financial resources impede the adoption of AI.

    “According to the resource-based view (RBV), an organization’s financial resources impede the adoption of AI in this manner (Tarannum and Farheen, 2020).”
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    The perceived benefits of adopting AI can be assessed based on its functionality, cost, and predicted benefits.

    “The perceived benefits of adopting AI can be assessed based on its functionality, cost, and predicted benefits (Alam et al., 2016).”
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    The amount of knowledge in the HR department affects the adoption of new technologies.

    “Few studies on human resource systems suggest that the amount of knowledge in the HR department effects the adoption of new technologies (Stone et al., 2015).”
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    The results of readiness models are frequently skewed because of how organizations evaluate themselves.

    “Furthermore, because of how organizations evaluate themselves, the results of readiness models are frequently skewed (Fischer et al., 2020).”
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    Successful use of AI has resulted in the reduction of numerous expenses, the effective and efficient use of resources, and the attainment of goals.

    “Additionally, the successful use of AI has resulted in the reduction of numerous expenses, the effective and efficient use of resources, and the attainment of goals (Hedt and Pagano, 2011; Muduli and Trivedi, 2020).”
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    Interpretationreasoning

    Larger companies will employ more HRM-related AI solutions.

    “AI use in the workplace is still in its early stages, and larger enterprises currently have more resources than smaller firms; thus, we believe that larger companies will employ more HRM-related AI solutions.”
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    AI's perceived benefits diminish resistance to its adoption.

    “It has been discovered that AI’s perceived benefits diminish resistance to its adoption (Hradecky et al., 2022).”
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    Before successfully adopting new technologies, organizations must be prepared and have the necessary capabilities.

    “However, before successfully adopting new technologies, organizations must be prepared and have the necessary capabilities (Daradkeh, 2019).”
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    Company size, level of formalization, resource availability, and managerial support influence an organization's readiness to adopt new technologies.

    “According to the previous study, the characteristics such as a company’s size (Kaufman, 2015), the level of formalization, resource availability, and managerial support, all influence its readiness to adopt new technologies (Patel and Cardon, 2010).”
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    The level of technological readiness and awareness of an organization's employees are major variables in the adoption of artificial intelligence.

    “The level of technological readiness and awareness of an organization’s employees are also major variables in the adoption of artificial intelligence.”
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    How people perceive AI is determined by how it works, how much it costs, and what features it includes.

    “As a result, how people perceive AI is determined by how it works, how much it costs, and what features it includes (Pillai and Sivathanu, 2020).”
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    Adopting AI in human resource operations can lead to greater efficiency and resource optimization.

    “Adopting AI in human resource operations can lead to greater efficiency and resource optimization (Sharma et al., 2022b).”
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    AI is vital in optimizing recruitment systems as it simplifies and reduces the cost of tasks, like application screening, while simultaneously enhancing the hiring quality.

    “AI is vital in optimizing recruitment systems as it simplifies and reduces the cost of tasks, like application screening, while simultaneously enhancing the hiring quality (Muhaimin et al., 2020).”
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    AI is widely recognized as a crucial growth driver that enhances business structures.

    “AI is widely recognized as a crucial growth driver that enhances business structures (Wamba et al., 2017; Kshetri, 2020; Kambur and Akar, 2022).”
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    Premisereasoning

    AI-powered chatbots have the potential to streamline and expedite the screening and filtering of applications, resulting in significant savings in time and effort for HR professionals.

    “AI-powered chatbots have the potential to streamline and expedite the screening and filtering of applications, resulting in significant savings in time and effort for HR professionals.”
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    AI is perceived in human resource management as a means of expediting personnel hiring and selection processes while avoiding favoritism or nepotism.

    “AI is perceived in human resource management as a means of expediting personnel hiring and selection processes while avoiding favoritism or nepotism (Reddy et al., 2019).”
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    When AI is used correctly, organizations can build their cultures and strong cultures can help firms use AI more effectively.

    “When AI is used correctly, organizations can build their cultures and that strong cultures can help firms use AI more effectively (Vrontis et al., 2022).”
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    An organization's culture is a set of shared principles that distinguishes it from other organizations, which creates effective HRM practices.

    “An organization’s culture is a set of shared principles that distinguishes it from other organizations, which creates effective HRM practices.”
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    Due to intense competition, AI adoption may become a strategic necessity.

    “Rizk (2020) asserted that due to intense competition, AI adoption may become a strategic necessity.”
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    Study design and conductreasoning

    Incomplete and incorrect responses were eliminated to reduce desirability biases, leaving 88 responses for analysis.

    “To reduce the desirability biases, incomplete and incorrect responses were eliminated from the study, which, thus, left us with 88 responses for analysis purpose.”
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    Study design and conductown data

    The study circulated 250 questionnaires and received 150 responses, a response rate of 60%, which is within the threshold limit.

    “This study circulated 250 questionnaires and received 150 responses; a response rate of 60% was recorded, which is within the threshold limit.”
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    Study design and conductcitation

    The study used a self-administered structured questionnaire in English with a 5-point Likert scale, which helped reduce response biases.

    “A self-administered structured questionnaire in English language with the Likert scale was used, following the 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree); both of these helped in reducing response biases (Anjum et al., 2022; Rahman et al., 2022).”
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    The AI approach can facilitate staff development and improve their performance.

    “This approach can facilitate staff development and improve their performance (Vinichenko et al., 2020).”
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    Pressure from trade partners and pressure from competitors are significant determinants of whether or not a corporation will use AI adoption.

    “Hossain et al. (2017) and Malik et al. (2021) discovered that pressure from trade partners and pressure from competitors are significant determinants of whether or not a corporation will use AI adoption.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Hossain et al., 2017 source

      Not checked in the free run.

    • cites Malik et al., 2021 source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation

    Competitive pressure has a significant impact on the rate of AI adoption.

    “Competitive pressure has a significant impact on the rate of AI adoption.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    AI and related technologies have a significant impact on how businesses operate and are structured.

    “AI and related technologies, such as machine learning, neural networks, and virtual assistants, have a significant impact on how businesses operate and are structured (Hangl et al., 2022).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Hangl et al., 2022 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    A pilot study was conducted among pharmaceutical employees to measure the effectiveness of the questionnaire.

    “for measuring the effectiveness of the questionnaire, a pilot study was conducted among pharmaceutical employees.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Premisecitation

    The best strategy for maximizing the return on AI investments is to change organizational cultures.

    “Something that all members of the group agree on is that the best strategy for maximizing the return on AI investments is to change organizational cultures (Cruz-Jesus et al., 2018).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Cruz-Jesus et al., 2018

      Not checked in the free run.

  • May combine two claims.

    Study design and conductown data

    All VIF values in Table 4 are below 5, so collinearity is not a concern in this study.

    “As shown in Table 4, all the values of the VIF are below the minimum acceptable value of 5 (Hair et al., 2019). Thus, collinearity is not a concern in this study.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Hair et al., 2019 source

      Not checked in the free run.

  • May combine two claims.

    Study design and conductcitation

    HTMT values are less than 0.90 for all reflective constructs, confirming discriminant validity and indicating no correlation among the constructs.

    “the heterotrait–monotrait ratio (HTMT), shown in Table 3, also confirms discriminant validity, where the values are less than 0.90 (Roemer et al., 2021; Hair and Alamer, 2022) of all the reflective constructs, indicating that there is no correlation among the constructs.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Roemer et al., 2021 source

      Not checked in the free run.

    • cites Hair and Alamer, 2022 source

      Not checked in the free run.

  • The quote only partly states this claim.

    Interpretationcitation

    AI adoption partially mediates the relationship between organizational preparedness and effective HRM.

    “this study proves another partial mediation of OP ->AI ->EH AI adoption on the relationship between organizational pressure and effective HRM (H 16 ) (Hmoud, 2021) (b = 0.15; t = 3.98; p = 0.00) as organizational pressure is proven to have a direct impact on effective HRM (H 7 ) (Agarwala, 2003).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Hmoud, 2021

      Not checked in the free run.

    • cites Agarwala, 2003 source

      Not checked in the free run.

  • May combine two claims.

    Study design and conductcitation

    Indicator loadings crossed the minimum threshold of >0.70, ranging from 0.76 to 0.91, indicating that the construct explained more than 70% of indicator variance and ensuring construct reliability.

    “Table 2 exhibits the details of loadings crossing the minimum threshold value of >0.70 ranging from 0.76 to 0.91, indicating that the construct has explained more than 70% of indicator variance, thus ensuring construct reliability (Hair et al., 2019).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Hair et al., 2019 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    A small sample size is ideal for PLS-SEM analysis since the population being studied is homogeneous.

    “A small sample size is ideal for PLS-SEM analysis since the population being studied is homogeneous (Cochran, 1977).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Cochran, 1977

      Not checked in the free run.

  • May combine two claims.

    Study design and conductown data

    None of the VIF values in Table 1 were above the threshold of 5, indicating the absence of multicollinearity.

    “Using the variance inflation factor (VIF) to measure multicollinearity, the researchers found that none of the values in Table 1 were above the threshold of 5, indicating the absence of multicollinearity (Hair et al., 2017a).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Hair et al., 2017a

      Not checked in the free run.

  • May combine two claims.

    Study design and conductcitation

    Cronbach alpha values range from 0.87 to 0.93 and composite reliability values are 0.91-0.94, both above the minimum threshold of >0.70, establishing internal consistency.

    “The value of Cronbach alpha ranges from 0.87 to 0.93, and composite reliability values are 0.91–0.94; both values are quite above the minimum threshold value of >0.70 (J. Hair and Alamer, 2022), thus establishing the internal consistency.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites J. Hair and Alamer, 2022 source

      Not checked in the free run.

  • May combine two claims.

    Study design and conductown data

    AVE values range from 0.67 to 0.77, above the acceptable limit of 0.50, establishing convergent validity.

    “In this study, AVE values range from 0.67 to 0.77, which is quite above the acceptable limit, as shown in Table 1, thus establishing convergent validity.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationcitation

    Competitive pressure, organizational preparedness, and technological readiness could not establish relationships with AI adoption because availability of resources and commitment is necessary for structural changes, and government spending on healthcare was just 1% of GDP before the pandemic.

    “However, competitive pressure, organizational preparedness, and technological readiness could not establish relationships with AI adoption as the availability of resources and commitment is a must for bringing structural changes to the organization (Garrison et al., 2015); in addition to this, overall, the government spending on healthcare sector was just 1% of the GDP before the pandemic (Goswami”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Garrison et al., 2015 source

      Not checked in the free run.

    • cites Goswami, 2021 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). The quote only partly states this claim.

    Interpretationown data

    There is a dearth of female workforce in India.

    “there is a dearth of female workforce in India.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    AI-based recruiting allows recruiters to spend more time with the best talent management candidates.

    “One of the most amazing aspects of AI-based recruiting is that it allows recruiters to spend more time with the best talent management candidates (van Esch et al., 2021).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites van Esch et al., 2021 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The questions and ideas used in this study came from studies conducted in Western countries.

    “Although the questions and ideas used in this study came from the studies conducted in Western countries”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

    Interpretationnone given

    AI will become a critical tool for achieving HRM goals, such as hiring top talent, retaining them for longer periods of time, and developing their leadership potential.

    “AI will become a critical tool for achieving HRM goals, such as hiring top talent, retaining them for longer periods of time, and developing their leadership potential.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: AI-based HRM systems excel at many tasks, including hiring candidates faster, spending less money, and making the best use of available resources.
    • Rests on an assumption: AI shows potential to expand the number and diversity of hiring metrics.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The study only looked at data from major cities.

    “First, we only looked at data from major cities.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    Only 160 human resource managers from Indian pharmaceutical companies were involved.

    “Only 160 human resource managers from Indian pharmaceutical companies were involved.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    When AI is used in hiring, recruiters can access a larger prospect pool while doing less administrative work.

    “When artificial intelligence is used in hiring, recruiters can access a larger prospect pool while doing less administrative work (Vardarlier and Zafer, 2020).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Vardarlier and Zafer, 2020

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    H2, H5, H6, H9, and H10 are not supported directly, but with the mediation of AI, full mediation was shown to be established among the non-supported hypotheses.

    “as seen in Table 5, H2, H5, H6, H9, and H10 are not supported directly from the perspective of the Indian pharmaceutical sector, but with the mediation of artificial intelligence, full mediation was shown to be established among the non-supported hypotheses.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Study design and conductnone given

    The survey was conducted in accordance with the rules to ensure high-quality data.

    “the survey was conducted in accordance with the rules to ensure high-quality data.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    AI has had a significant impact on HRM practices, but little research has been conducted to determine how well it works within organizations.

    “Artificial intelligence has had a significant impact on HRM practices, but little research has been conducted to determine how well it works within organizations (Wamba et al., 2017).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Wamba et al., 2017 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    Because the study was limited to India, the current findings cannot be applied to other countries.

    “Because our study was limited to India, the current findings cannot be applied to other countries.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    AI shows potential to significantly alter the workplace and provide businesses with a competitive advantage.

    “AI shows potential to significantly alter the workplace and provide businesses with a competitive advantage (Tambe et al., 2019).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Tambe et al., 2019 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    AI's cognitive capabilities differ significantly from those of traditional e-HRM tools.

    “However, AI’s cognitive capabilities differ significantly from those of traditional e-HRM tools.”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites van Esch et al., 2021 source

      Not checked in the free run.

    • cites Vrontis et al., 2022 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The study only considered the pharmaceutical industry as a whole.

    “Second, we only considered the pharmaceutical industry as a whole.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Recruiters used to perform repetitive tasks manually, but AI would make some of these tasks obsolete.

    “Recruiters used to perform repetitive tasks manually, but AI would make some of these tasks obsolete (Pillai et al., 2022).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Pillai et al., 2022 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Recruiters can delegate mundane tasks to AI systems, freeing up their time and resources for more strategic issues.

    “Recruiters can now delegate these mundane tasks to AI systems, freeing up their time and resources for more strategic issues (Pillai and Sivathanu, 2020).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Pillai and Sivathanu, 2020 source

      Not checked in the free run.

78 of 78

  • The paper relies on this without support.

    The exogenous variables considered in the current study have not been studied together in any previous research.

    “However, several studies have proved the significance level of AI adoption in developing effective HRM, but exogenous variables considered in the current study have not been studied together in any previous research.”

  • The paper relies on this without support.

    The current model of the study has not been applied in Indian pharmaceutical companies previously.

    “Moreover, the current model of the study is applied in Indian pharmaceutical companies, which was not considered previously.”

  • The authors state this as an assumption.

    AI is regarded as a high-tech tool.

    “Third, we regard AI as a high-tech tool.”

  • The paper relies on this without support.

    AI-based HRM systems excel at many tasks, including hiring candidates faster, spending less money, and making the best use of available resources.

    “AI-based HRM systems excel at many tasks, including hiring candidates faster, spending less money, and making the best use of available resources.”

  • The paper relies on this without support.

    AI shows potential to expand the number and diversity of hiring metrics.

    “AI shows potential to expand the number and diversity of hiring metrics.”
5 of 5

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Perceived benefit, perceived usefulness, usability, ease of use, usefulness, accuracy, and reliability of the output of AI are key contributors to adoption.

    “Perceived benefit, perceived usefulness, usability, ease of use, usefulness, accuracy, and reliability of the output of the AI are key contributors to adoption [19,45,62-67].”

    From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [63]

    The Conclusion says perceived benefits aid AI adoption in pharmaceutical companies, and Theoretical background and hypothesis development identifies perceived usefulness and ease of use as determinants of technology acceptance. The passages do not identify usability or the accuracy and reliability of AI output as adoption contributors; the study’s own findings also concern a narrower HRM setting than the unqualified claim. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Organizational-culture mediation t-statistic conflicts with its standard error

    Major

    Using Table 6's stated |O/STDEV| calculation gives 0.4 rather than 2.44, changing the stated significance of this mediation.

    Reported 2.44 · recomputed 0.4

  • Perceived-benefits mediation t-statistic conflicts with its standard error

    Major

    Table 6 defines the t-statistic as |O/STDEV|. Its reported coefficient and standard error yield about 0.455, not 3.37, changing the stated significance of this mediation.

    Reported 3.37 · recomputed 0.454545

  • Organizational-preparedness mediation t-statistic conflicts with its standard error

    Major

    Using Table 6's stated |O/STDEV| calculation gives about 0.556 rather than 3.98, changing the stated significance of this mediation.

    Reported 3.98 · recomputed 0.555556

  • Technological-readiness mediation t-statistic conflicts with its standard error

    Major

    Using Table 6's stated |O/STDEV| calculation gives about 0.333 rather than 3.59, changing the stated significance of this mediation.

    Reported 3.59 · recomputed 0.333333

  • Competitive-pressure mediation t-statistic conflicts with its standard error

    Major

    Using Table 6's stated |O/STDEV| calculation gives about 0.316 rather than 2.24, changing the stated significance of this mediation.

    Reported 2.24 · recomputed 0.315789

  • Conclusion says all hypothesized relationships were confirmed

    Major

    The conclusion claims confirmation of every hypothesized relationship, whereas Table 5 explicitly marks H2 as not supported, along with several other hypotheses.

  • Discussion calls a negative competitive-pressure effect positive

    Major

    The discussion includes competitive pressure among variables positively related to effective HRM, but Table 5 reports a significant negative coefficient for that path.

  • Lowest loading does not explain over 70% of indicator variance

    Minor

    The stated loading range includes 0.76. Squaring that loading gives 0.5776, or 57.76% of indicator variance, not more than 70%.

    Reported 0.7 · recomputed 0.5776

  • TTF is also identified as the technology acceptance model

    Minor

    The paper expands TTF as "task-technology fit" but later places "technology acceptance model" in parentheses after TTF, assigning the same abbreviation to two different models.

  • H2 names different predictors in the hypothesis and results

    Minor

    The stated H2 concerns organizational preparedness and AI adoption, while Table 5 assigns H2 to competitive pressure and AI adoption. The results use a different numbering of the proposed hypotheses.

  • Insignificant H9 has an effect size above the stated threshold

    Minor

    The text says insignificant hypotheses have effect sizes below 0.02, but H9 has an insignificant p-value of 0.13 and an f2 of 0.039.

11 of 11

The citation lists haven’t been fetched yet

References 104 · Cited by 33

Research works printed but not linked: 9

  1. Ahmad Salleh, 2018, An implementation of Sec-TOE framework: identifying security determinants of big data solutions adoption an implementation of sec-TOE framework: identifying security determinants of big data, PACIS 2018 Proc., 211
  2. Alghnimi, 2020, The mediating effect of employee engagement between sustainable hrm practices and job performance, Int. J. Innovation, Creativity Change, 13, 643
  3. Brynjolfsson, 2017, Artificial intelligence, for real, Harv. Bus. Rev., 1, 1
  4. Cochran, 1977, Sampling techniques
  5. Esen, 2014, Effects of technology readiness on technology acceptance in e-HRM: mediating role of perceived usefulness, J. Knowl. Econ. \& Knowl. Manag., 9, 7
  6. Guenole, 2018, The business case for AI in HR.
  7. Pandey, 2020, Exploring the role of artificial intelligence (AI) in transforming HR functions: an empirical study in the Indian context, Int. J. Sci. Res. Eng. Dev
  8. Risher, 2017, The robustness of PLS across disciplines, Acad. Bus. J., 1, 47
  9. Sharma, Review on digitalisation and artificial intelligence in human resource function of energy sector, Water Energy Int., 65, 38

Authors
Goswami, M., Jain, S., Alam, T., Deifalla, A. F., Ragab, A. E., & Khargotra, R.
Affiliations
  • GLA University (IN)
  • Central Building Research Institute (IN)
  • Future University in Egypt (EG)
  • King Saud University (SA)
  • University of Pannonia (HU)
Corresponding author
Alam, Tabish
ORCID
Venue
Frontiers in Pharmacology
Publisher
Frontiers Media SA
Published
2023-11-14
Volume
14
Pages
1215706-1215706
ISSN
1663-9812 (ISSN-L)
DOI
10.3389/fphar.2023.1215706
PMC
PMC10682089
OpenAlex
W4388657557
Semantic Scholar
ec77aca4ea6f76a5a8f09eda8472628b6a263cdf
Cited by
33
References
104
Citations by year
2026: 16 · 2025: 7 · 2024: 10
FWCI
9.57
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Pharmacology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
King Saud University (RSPD2023R711)
Topics
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Employer Branding and e-HRM
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Keywords
artificial intelligence adoption, human resource management practices, pharmaceutical industry, task-technology fit, technology readiness, perceived benefits of AI, organizational preparedness, Indian pharmaceutical sector

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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