edits for disser
Prettygirl122747
CHAPTER2.docx9
CHAPTER 2: LITERATURE REVIEW
Introduction
An increasing amount of attention has been paid to incorporating AI technology into HRM processes in the quickly changing landscape of organizational decision-making. Enterprises must comprehend how AI technologies will affect HR decision-making processes as they work to improve their competitive advantage and meet the demands of the digital age. The literature review is a fundamental part of this effort, providing information on the state of knowledge at the moment and guiding future paths for study and application. In organizational decision-making, Jarrahi (2018) highlights the establishment of a symbiotic relationship between humans and AI systems, emphasizing the potential for AI technologies to augment human talents rather than replace them completely. This perspective underscores the need to explore how AI can be effectively integrated into HR practices to optimize decision-making outcomes.
Similarly, in the rapidly evolving field of corporate decision-making, AI technology integration into HRM procedures has received more and more attention. To maintain a competitive edge and adapt to the demands of the digital age, businesses must understand how AI technology will impact HR decision-making procedures. An essential component of this endeavor is the literature review, which informs readers about the current state of knowledge and directs future research and application directions. Jarrahi (2018) emphasizes the development of a symbiotic relationship between humans and AI systems in organizational decision-making, stressing the potential for AI technologies to augment human talents rather than fully replace them. Furthermore, Huang and Peissl (2023) investigate how artificial intelligence (AI) can revolutionize knowledge and decision-making. Their work highlights the necessity for enterprises to redefine old methods to decision-making processes and embrace AI technologies in order to adapt to this paradigm change. Given these contributions, the literature review provides an essential framework for integrating current research and clarifying the intricacies of AI technology in HR decision-making. This review attempts to guide future research endeavors in this developing field and inform evidence-based practices by analyzing ideas from a variety of scholarly viewpoints.
Theoretical framework
The integration of AI technology in HR decision-making has emerged as a central topic of academic research and corporate practice. Our comprehension of this phenomena is greatly influenced by theoretical viewpoints, which provide frameworks for analyzing the effects of AI technology on HR procedures. The integration of AI technology into HR decision-making processes is one example of a complex phenomenon that may be interpreted and analyzed by researchers and practitioners using theoretical frameworks as conceptual lenses. These frameworks offer guiding principles and assumptions that help elucidate the underlying mechanisms and dynamics at play.
Theoretical viewpoints provide insightful information about how HR departments might use AI technology to improve the efficacy, efficiency, and equity of decision-making. This section provides an overview of key theoretical perspectives relevant to AI integration in HR decision-making.
Resource-Based View
RBV holds that companies have unique resources and competencies that can offer them a competitive advantage and boost productivity (Iruthayasamy & Iruthayasamy, 2021). According to this point of view, AI technologies are important tools that firms may use to streamline HR decision-making processes and gain a sustained competitive edge. Collins (2021) expands on the RBV model by emphasizing the strategic role that HRM plays in leveraging organizational resources for better performance. This highlights how important it is to align AI technologies with strategic HR objectives in order to maximize their impact on an organization's productivity and competitiveness.
Figure 1: RBV Framework
Source: Jurevicius, (2023)
As seen in the figure above, the concept of resource heterogeneity and immobility, central to RBV, also applies to the adoption and utilization of AI technologies in HR decision-making. According to Jurevicius (2023), heterogeneity refers to the idea that organisations have varying resource bundles, which enables them to adopt unique tactics and obtain a competitive edge. In a similar vein, the immobility of resources suggests that firms find it difficult to duplicate the resources of competitors, especially intangible assets like AI algorithms and knowledge. In order to obtain sustainable competitive advantages in HR decision-making, organisations must evaluate if their AI technologies are valuable, rare, expensive to copy, and non-substitutable using the VRIO framework within RBV. Organisations must also make sure that they are set up to take full advantage of these AI resources, coordinating them with strategic HR goals to optimise their influence on overall performance (Jurevicius, 2023).
Ployhart (2021) in the exploration of the RBV framework's idea of resources, highlights the significance of comprehending the ways in which resources affect organizational performance. By leveraging AI-enabled HR technologies that harness the power of predictive analytics, machine learning, and natural language processing, organizations may gain unparalleled precision and agility in making data-driven choices and gain deeper insights into worker dynamics (Gueler & Schneider, 2021). This demonstrates the necessity for businesses to strategically manage and use AI technology as tools to inform HR decisions and produce better performance results.
In order to obtain sustainable competitive advantages in HR decision-making, organisations must evaluate if their AI technologies are valuable, rare, expensive to copy, and non-substitutable using the VRIO framework within RBV (Jurevicius, 2023). To fully utilise these AI tools and optimise their impact on organisational performance, organisations must also make sure that they are strategically aligned with HR goals.
Diffusion of Innovations Theory
The Diffusion of Innovations theory is a prominent theoretical framework that sheds light on how AI technologies are integrated into HR decision-making. The method via which new technologies are embraced and dispersed within a social system is clarified by Everett Rogers' groundbreaking work in this field (Curtis, 2020). The theory is as shown below;
Figure 2: Diffusion of Innovations theory
Source: Drea Burbank. (2018)
The Diffusion of Innovations theory helps us understand where AI technologies fit on the adoption curve within the HR domain. As seen in the above image, early adopters of AI technologies—such as tech-savvy HR professionals and visionary leaders may embrace them at first if they see the potential advantages. But there's a "chasm" between early adoption and general use, where the technology needs to demonstrate its worth and usefulness to a wider range of pragmatists, conservatives, and sceptics in the HR community (Drea Burbank, 2018) By utilizing this theory, scholars can get a deeper comprehension of the variables impacting the integration of AI in HR and, consequently, the adoption dynamics and diffusion processes related to these technologies. When it comes to AI acceptance in HR, the Diffusion of Innovations theory examines the many stages of adoption—from knowledge to confirmation—as well as the variables affecting each stage. Tuffaha (2022) highlights the application of this theory in analyzing the decision-making processes of HR professionals, emphasizing its relevance in understanding the adoption aspects of AI in HR management.
Ghosh, Majumder, and Peng (2023) employ Rogers' Innovation Diffusion Theory to examine the adoption process. Their research emphasizes how crucial it is to comprehend adopter traits, how AI technologies are seen, and how communication channels affect adoption decisions. Researchers can use this theory to identify the factors that encourage and hinder AI adoption in HR, which can then be used to influence organizational policies and interventions meant to encourage adoption and implementation. Chen (2024) expands on Rogers' Innovation Diffusion Theory by examining how new teaching approaches are adopted in secondary education. The principles of diffusion hold true even in different contexts, providing valuable insights on the adoption and spread of innovations in educational environments. Analyzing the spread of AI technology can also help HR decision-makers overcome obstacles, resolve issues, and promote an innovative culture within their firms.
Technology Acceptance Model (TAM)
The TAM model proposed by Davis was a frequently utilised powerful tool that explained the influential aspects when consumers adopted new devices or technologies for data communication in the field (Na, et al., 2022). The figure below shows the TAM
Figure 3: TAM Model
Source: Na, et al., (2022)
As illustrated in the above-mentioned Figure, TAM postulates that users' opinions regarding the utility and usability of new technology are impacted by a range of external circumstances, which in turn mould their attitudes and intentions regarding its adoption (Na, et al., 2022). Perceived utility, as used in this study, relates to how much HR professionals think AI-enabled HR solutions can improve decision-making procedures and overall job performance. This is in line with the goal of our study, which is to determine how HR professionals view the value of AI in improving their ability to make decisions by looking at how AI technologies are integrated into HR decision-making processes.
TAM attempts to understand people's acceptance and adoption of new technologies based on perceived usefulness and ease of use (Zaineldeen et al., 2020). TAM provides information on the adoption and application of AI technologies in the HR environment by HR personnel and professionals. TAM defines perceived utility as the extent to which individuals believe AI-enabled HR solutions could improve decision-making and work performance. On the other side, perceived ease of use describes how people view the accessibility and usability of AI technologies (Kamal et al., 2020). Additionally, TAM offers a framework for comprehending the elements impacting people's views and intentions regarding implementing AI in HR, assisting businesses in encouraging their effective integration.
The organizational characteristics and technology environment that are pertinent to AI adoption in HR influence the external variables of TAM that we employed in our study. These external factors are shown in the table below, along with factors like organizational characteristics like scale, management structure, and culture, advantages over challenges of adoption, and technical appropriateness (Na, et al., 2022). Understanding these elements is crucial to figuring out how prepared and capable companies are to integrate AI into their HR decision-making procedures.
Figure 4: Technology-Organisation-Environment Framework
Source: Na, et al., (2022).
Moreover, research indicates that effective adoption of new technology depends on organizational size, resource availability, managerial leadership, and communication. The pace and strategies used in AI adoption are influenced by these organizational characteristics, and this has a direct effect on how AI technologies are incorporated into HR decision-making procedures. Thus, this research intends to provide insights into the factors impacting the acceptability and deployment of AI technologies in HR within organizational contexts by taking these external variables into account within the TAM framework.
Ethical Decision-Making Theory
This theory examines at how individuals and groups come to moral judgments and decisions (Schwartz, 2016). In the context of AI integration in HR, this theory provides a framework for understanding the moral conundrums and ethical concerns brought up by AI-driven decision-making processes. Contextual factors, organizational norms, and personal values all influence ethical decisions, according to the Ethical Decision-Making Theory (Banks et al., 2022).
Furthermore, this theory highlights how crucial it is to encourage ethical responsibility and knowledge among HR specialists and organizational leaders. Organizations can reduce possible dangers and guarantee that AI technologies are implemented responsibly and ethically by integrating ethical considerations into AI deployment strategies (Konda, 2022). Additionally, the ethical decision-making theory emphasizes that in order to resolve ethical issues and promote confidence in AI-driven HR decision-making processes, stakeholder engagement and transparent communication are essential.
In the context of their research, this theory contributes significantly by highlighting the importance of ethical responsibility and awareness among HR professionals and organizational leaders. According to Lehner et al. (2022), who explored the ethical challenges of AI-based decision-making in accounting and auditing, understanding the ethical implications of AI technologies is crucial for ensuring responsible deployment in HR contexts. Moreover, In order to minimize risks and guarantee responsible implementation, Ethical Decision-Making Theory emphasizes how important it is to incorporate ethical considerations into AI deployment plans. This is consistent with the results of Winata et al. (2020), who highlighted the value of making moral decisions based on literature from the past. Since the researchers' goal is to investigate the difficulties and moral issues surrounding the use of AI technology in HR decision-making, these investigations bolster their goals.
Furthermore, the ethical decision-making theory highlights stakeholder engagement and open communication as crucial elements for addressing moral dilemmas and building confidence in AI systems. This component is very pertinent to the study since the goal is to find out how satisfied employees are with the fairness and openness of AI-driven HR procedures. Through the integration of findings from these studies, scholars can investigate strategies for productive stakeholder engagement and open communication on the moral ramifications of AI implementation in HR decision-making within enterprises. In conclusion, Ethical Decision-Making Theory offers important insights into the moral implications of integrating AI into HR and offers recommendations for encouraging morally and responsibly in decision-making processes. By incorporating these insights into their research, the researchers can contribute to a deeper understanding of the ethical challenges and opportunities associated with the adoption of AI technologies in HR.
Theory Summary
The Resource-Based View (RBV), Technology Acceptance Model (TAM), Ethical Decision-Making Theory, and the Diffusion of Innovations theory offer valuable insights into AI integration in HR decision-making. In order to improve HR decision-making efficacy, RBV highlights AI technologies as strategic resources. It also emphasizes the necessity for businesses to invest in AI capabilities in order to obtain a competitive advantage. TAM clarifies elements that affect AI acceptance, like perceived utility and usability, and directs activities to remove adoption barriers and advance integration. The ethical decision-making theory ensures fairness and openness in HR procedures by emphasizing ethical issues in AI adoption. The dynamics of the adoption process are explained by the diffusion of innovations theory, which helps with the creation of plans to promote AI adoption throughout HR departments. When taken as a whole, these ideas offer a thorough knowledge of AI integration in HR, directing future research and organizational actions.
Empirical Literature Review
Integration of AI Technologies into HR Decision-Making Processes
The benefits of incorporating AI technology into HR decision-making processes, including hiring, performance reviews, and talent management, have been the subject of numerous studies. According to research, AI-driven solutions expedite the hiring process by automating the scheduling of interviews, resume screening, and applicant searches (Gupta & Mishra, 2023). AI systems evaluate a candidate's abilities, character, and cultural fit as well, which improves the efficacy and objectivity of recruiting choices. According to Fagarasan et al. (2023), AI-enabled performance evaluation systems support data-driven coaching and development programs, detect performance trends, and offer real-time feedback. AI systems also help with people management by predicting attrition rates, identifying high-potential individuals from employee data, and customizing professional development plans. These studies indicate that artificial intelligence (AI) technologies are being more deeply incorporated into different HR roles, enhancing organizational effectiveness and decision-making processes.
Impact of AI-Enabled HR Tools on Decision-Making
Research has looked at how AI-enabled HR technology affects decision-making's efficiency, accuracy, and reduction of bias in comparison to conventional approaches. Studies show that AI systems outperform humans in predicting job performance and cultural fit, which improves hiring decisions (Chen, 2022). Additionally, AI-driven solutions speed up the decision-making process by rapidly discovering patterns and trends in massive information through analysis. Yarger et al. (2020) have expressed concerns over algorithmic bias and the possibility that AI would legitimize discriminatory hiring and performance review practices. Despite these difficulties, empirical evidence indicates that AI-enabled HR technology may improve the efficiency, accuracy, and reduction of bias in a variety of HR operations.
Moreover, empirical research has explored the wider implications of AI-powered HR technology, going beyond the precision and effectiveness of decision-making. For example, Chen et al., (2022) shows that AI systems can help with improved talent discovery and retention techniques in addition to being able to predict job performance and cultural fit more precisely than humans. AI-driven solutions have also been demonstrated to help with proactive workforce planning by spotting new talent requirements and skill shortages. Nonetheless, worries about algorithmic bias and the possibility that AI would support discriminatory behaviors in HR procedures continue. Notwithstanding these obstacles, empirical data indicates that AI-powered HR technology can potentially enhance the general efficacy, accuracy, and equity of decision-making in a variety of HR-related domains.
Challenges and Ethical Considerations
Using AI in HR decision-making brings with it a number of difficulties and moral conundrums that companies must resolve. In their artificial intelligence algorithmic approach to ethical decision-making in HR procedures, Rodgers et al. (2023) draw attention to how difficult it is to guarantee that moral standards are maintained while using AI-driven systems. According to Radonjić, Duarte, and Pereira (2022), HR managers must prioritize strong ethical frameworks and decision-making processes in order to overcome issues relating to decisiveness and ethical considerations while using AI technologies. In their discussion of the opportunities and problems that AI presents for global HRM, Budhwar et al. (2022) stress the significance of resolving ethical issues in order to uphold the reputation and integrity of the company. Additionally, Slimi and Carballido (2023) examine international AI ethics regulations and draw attention to the moral dilemmas raised by the use of AI, notably in higher education. All things considered, these studies highlight how critical it is to proactively address issues and moral dilemmas in order to guarantee the ethical and responsible application of AI technology in HR decision-making processes.
Employee Satisfaction with AI-Driven Processe
Research has examined how satisfied workers are with the fairness and transparency of AI-powered HR decision-making procedures. According to Khair et al. (2020), workers believe AI-enabled HR decision-making to be more unbiased and efficient than human judgment. However, Madancian and Taherdoost (2023) draw attention to the necessity for businesses to deal with any possible issues pertaining to the integration of AI in HR, such as issues with transparency and justice. Zhou et al. (2023) highlight the possible "dark side" of AI-enabled HRM, pointing out that some algorithmic aspects of AI may erode employee happiness and confidence despite the efficiency improvements. In particular, workers might find AI-driven procedures to be opaque and prejudiced, which would make them unhappy with the way HR decisions are made. Overall, these results highlight how crucial it is for businesses to put employee involvement, fairness, and transparency first when implementing AI technology in HR decision-making to guarantee acceptance and happiness among staff members.
Braganza et al. (2022) examine gigification and job engagement, emphasizing the ways in which AI-enabled system automation can influence employee satisfaction in a moderating manner. This study emphasizes how crucial it is to take into account contextual elements when analyzing the relationship between artificial intelligence (AI) and employee satisfaction, such as the type of work arrangements and degree of automation. Organizations can more effectively customize their AI adoption strategies to improve employee engagement and satisfaction by recognizing the complexity of these interactions. Prentice et al. (2020) investigate how consumer satisfaction, loyalty, and staff service quality are affected by artificial intelligence. According to their research, AI-driven improvements in staff service quality can have a favorable impact on client loyalty and happiness. But this also draws attention to a possible conflict between guaranteeing staff happiness and well-being and maximizing AI for customer-centric results. Organizations must strike a balance between leveraging AI to improve customer experiences and supporting employees in adapting to AI-driven changes.
Furthermore, Chakraborty et al. (2023) provide an AI-driven method for enhancing employee happiness that is modeled after Maslow's Hierarchy. This novel viewpoint highlights how crucial it is to take into account the basic requirements and motivations of employees while integrating AI. Organizations can improve workplace satisfaction and well-being by coordinating AI activities with employees' psychological needs and goals. Böhmer and Schinnenburg (2023) conclude by critically examining AI-driven HRM as a means of enhancing organizational capabilities. Their study emphasizes how important it is to have a comprehensive grasp of how AI could change organizational dynamics and HR procedures. Through a critical assessment of the effects of AI integration on worker autonomy, job satisfaction, and organizational culture, businesses can find ways to efficiently utilize AI technology while reducing risks and obstacles.
These studies emphasize how crucial it is to approach AI-driven HR procedures critically, taking into account both the advantages and disadvantages they may have for worker happiness and organizational efficacy. Through strategic decision-making and careful analysis, firms may leverage artificial intelligence (AI) to improve employee experiences and propel organizational growth.
Literature Gap Analysis
While existing literature provides valuable insights into various aspects of AI integration in HR decision-making, several gaps remain that warrant further exploration. The lack of attention paid to the moral ramifications of AI adoption in HR is one obvious gap. Although several studies stress the significance of ethical issues, there is a dearth of thorough research on how businesses handle moral conundrums brought on by AI-driven HR decision-making (Rodgers et al., 2023). Furthermore, although research has looked at how satisfied employees are with AI-driven processes (Khair et al., 2020), little has been done to particularly look into how fair and transparent employees see AI-enabled HRM to be (Zhou et al., 2023). This disparity emphasizes the necessity of doing empirical research to investigate workers' perceptions and experiences about the impartiality and openness of AI-driven HR decision-making.
While existing studies provide insights into immediate outcomes such as efficiency improvements and bias reduction, there is limited understanding of the broader implications for organizational culture, employee well-being, and strategic HR management (Braganza et al., 2022). Longitudinal studies that monitor the effects of AI technology on organizational procedures and employee experiences over time are necessary to investigate these long-term implications. Furthermore, there is a deficiency in the literature regarding AI's potential and role in addressing new opportunities and challenges in HR management. It is necessary to investigate how AI technologies might assist HR professionals in navigating new difficulties including remote work, workforce diversity, and talent shortages, given the changing nature of work and the growing complexity of organizational contexts (Prentice et al., 2020). By filling up this knowledge vacuum, studies can offer insightful information about how AI might help HR departments adjust to shifting corporate environments.
The body of literature demonstrates how little is known about the organizational capacities required to take full use of AI-driven HRM. The technical components of AI implementation are the subject of some research, but little is known about the organizational procedures, leadership styles, and change management techniques needed for HR to successfully integrate AI (Böhmer & Schinnenburg, 2023). It will take multidisciplinary study that incorporates knowledge from organizational behavior, HR management, and technology adoption studies to close this gap.
Furthermore, a deficiency of research has been found in the literature gap analysis about the incorporation of AI technologies into different HR activities. There is little research that thoroughly examines AI integration across the whole HR spectrum, despite several studies concentrating on certain topics like hiring and performance reviews (Madancian & Taherdoost, 2023). This disparity emphasizes the necessity of doing comprehensive research on the effects of AI technology on HR decision-making procedures, including hiring, performance management, talent development, and employee relations. The research objectives have been designed based on the observed gaps in the literature, with a focus on overcoming these gaps to enhance our comprehension of AI integration in HR decision-making and its consequences for employee outcomes and organizational practices.
The Conceptual Framework
A conceptual framework provides a structured outline of the relationships between key variables and concepts under study (Shikalepo, 2020). The conceptual framework outlines the interactions between different elements that affect the uptake, efficacy, and moral implications of AI technologies in HR in the context of this study on AI integration in HR decision-making processes. Based on the research objectives, the study's conceptual framework seeks to demonstrate the links between HR decision-making, the dependent variable, and three independent variables.
Dependent Variable
HR Decision-Making
Independent Variables
Impact of AI-enabled HR tools
Integration of AI technologies
Ethical considerations
Figure 5: Theoretical Framework
Dependent Variable
The dependent variable in this conceptual framework is HR decision-making, which describes the process via which an organization makes operational and strategic decisions pertaining to human resource management. Decisions about hiring, performance reviews, talent management, and other HR tasks fall under this category. A variety of factors, such as the development and application of AI technology, impact HR decision-making.
Independent Variables
Integration of AI Technologies: The degree to which AI technologies are integrated into HR decision-making processes, including hiring, performance reviews, and talent management, is indicated by this variable. It includes implementing AI-driven tools and systems intended to enhance and improve HR procedures.
Impact of AI-Enabled HR Tools: The effects of AI technology on decision-making efficiency, accuracy, and bias reduction in comparison to conventional approaches are referred to as the impact of AI-enabled HR solutions. This variable looks at how the use of AI technologies affects the procedures and results of HR decision-making.
Ethical Considerations: Ethical considerations encompass the moral principles and values that guide decision-making processes within organizations. The ethical ramifications of AI adoption in HR are examined in this variable, along with issues with algorithmic bias, privacy, and justice. It also includes organizational procedures and regulations intended to guarantee the moral application of AI in HR.
The conceptual framework demonstrates how ethical considerations, the impact of AI-enabled HR tools, and the incorporation of AI technology into HR decision-making processes are related to one another. It makes the argument that the adoption and application of AI technology in HR has an impact on the efficacy and morality of decision-making. In particular, how well AI technologies are incorporated into HR procedures affects how decision-making is impacted by AI-enabled HR solutions. Furthermore, ethical considerations surrounding AI adoption in HR impact both the adoption and utilization of AI technologies and the impact of AI-enabled HR tools on decision-making.
Chapter Summary
In this chapter, a thorough analysis of the conceptual and theoretical foundations of AI integration in HR decision-making processes was provided. In order to better understand how AI is being adopted in HR, the chapter started out by examining important ideas including the RBV, Diffusion of Innovation Theory, TAM, and Ethical Decision-Making Theory. The strategic importance of AI technologies as useful tools that improve HR decision-making processes is emphasized by the RBV. To comprehend the acceptance and dissemination process of AI technology within enterprises, the diffusion of innovation theory was established. The TAM also provides insight into the elements affecting people's acceptance and use of AI-enabled HR solutions. Additionally, Ethical Decision-Making Theory highlights the significance of ethical decision-making in organizational activities and offers insights into the ethical issues related to AI adoption in HR. The linkages between the integration of AI technology, the effects of AI-enabled HR tools, and ethical considerations in HR decision-making were then illustrated by the development of a conceptual framework. This approach offers a conceptual foundation for examining the uptake, efficacy, and moral implications of AI in HR.
References (Chapter 2)
Banks, G. C., Knapp, D. J., Lin, L., Sanders, C. S., & Grand, J. A. (2022). Ethical decision making in the 21st century: A useful framework for industrial-organizational psychologists. Industrial and Organizational Psychology, 15(2), 220-235.
Böhmer, N., & Schinnenburg, H. (2023). Critical exploration of AI-driven HRM to build up organizational capabilities. Employee Relations: The International Journal, 45(5), 1057-1082.
Braganza, A., Chen, W., Canhoto, A., & Sap, S. (2022). Gigification, job engagement and satisfaction: the moderating role of AI enabled system automation in operations management. Production planning & control, 33(16), 1534-1547.
Budhwar, P., Malik, A., De Silva, M. T., & Thevisuthan, P. (2022). Artificial intelligence–challenges and opportunities for international HRM: a review and research agenda. The InTernaTIonal Journal of human resource managemenT, 33(6), 1065-1097.
Chakraborty, M., Singh, R. K., Hussein, T. M., Kler, R., Khan, S., & Mishra, S. (2023, November). Maslow's Hierarchy-Inspired AI-Driven Employee Satisfaction Improvement. In 2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS) (pp. 987-993). IEEE.
Chen, D. (2022). Artificial Intelligence (AI) in Employee Selection: How Algorithm-Based Decision Aids Influence Recruiters’ Decision-Making in Resume Screening (Doctoral dissertation, The University of Texas at Arlington).
Chen, R. (2024). A Study Applying Rogers’ Innovation Diffusion Theory on the Adoption Process of New Teaching Methods in Secondary Education. Research and Advances in Education, 3(2), 6-10.
Collins, C. J. (2021). Expanding the resource based view model of strategic human resource management. The International Journal of Human Resource Management, 32(2), 331-358.
Curtis, M. (2020). Toward understanding secondary teachers’ decisions to adopt geospatial technologies: An examination of Everett Rogers’ diffusion of innovation framework. Journal of Geography, 119(5), 147-158.
Drea Burbank. (2018, March 20). Diffusion of Innovation Theory. Medium; todreamalife.com. https://medium.com/todreamalife-com/diffusion-of-innovation-theory-ea94bfd0c18c
Fagarasan, C., Cristea, C., Cristea, M., Popa, O., & Pisla, A. (2023). Integrating Sustainability Metrics into Project and Portfolio Performance Assessment in Agile Software Development: A Data-Driven Scoring Model. Sustainability, 15(17), 13139.
Ghosh, S., Majumder, S., & Peng, S. L. (2023). An empirical study on adoption of artificial intelligence in human resource management. In Artificial Intelligence Techniques in Human Resource Management (pp. 29-85). Apple Academic Press.
Gueler, M. S., & Schneider, S. (2021). The resource-based view in business ecosystems: A perspective on the determinants of a valuable resource and capability. Journal of Business Research, 133, 158-169.
Gupta, A., & Mishra, M. (2023). Artificial Intelligence for Recruitment and Selection. In The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part B (pp. 1-11). Emerald Publishing Limited.
Huang, L., & Peissl, W. (2023). Artificial Intelligence—A New Knowledge and Decision-Making Paradigm?. In Technology Assessment in a Globalized World: Facing the Challenges of Transnational Technology Governance (pp. 175-201). Cham: Springer International Publishing.
Iruthayasamy, L., & Iruthayasamy, L. (2021). The resource-based view. Understanding Business Strategy: Confusion and Consensus, 63-74.
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586.
Jurevicius, O. (2023, October 24). Resource-Based View. Strategic Management Insight. https://strategicmanagementinsight.com/tools/resource-based-view/
Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212.
Khair, M. A., Mahadasa, R., Tuli, F. A., & Ande, J. R. P. K. (2020). Beyond Human Judgment: Exploring the Impact of Artificial Intelligence on HR Decision-Making Efficiency and Fairness. Global Disclosure of Economics and Business, 9(2), 163-176.
Konda, S. R. (2022). Ethical Considerations in the Development and Deployment of AI-Driven Software Systems. INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 6(3), 86-101.
Lehner, O. M., Ittonen, K., Silvola, H., Ström, E., & Wührleitner, A. (2022). Artificial intelligence based decision-making in accounting and auditing: ethical challenges and normative thinking. Accounting, Auditing & Accountability Journal, 35(9), 109-135.
Madancian, M., & Taherdoost, H. (2023, October). The Impact of Artificial Intelligence on Human Resource Management: Opportunities and Challenges. In International Conference Interdisciplinarity in Engineering (pp. 406-424). Cham: Springer Nature Switzerland.
Mago, B., Aideed, A. A., Al Ali, H. S., Alnuaimi, S. S., & Al Qahtani, F. R. (2022). Ethical Decision Making in Soft lifting-A UAE Based Case Study. International Journal for Global Academic & Scientific Research, 1(2), 06-15.
Na, S., Heo, S., Han, S., Shin, Y., & Roh, Y. (2022). Acceptance model of artificial intelligence (AI)-based technologies in construction firms: Applying the Technology Acceptance Model (TAM) in combination with the Technology–Organisation–Environment (TOE) framework. Buildings, 12(2), 90.
Ployhart, R. E. (2021). Resources for what? Understanding performance in the resource-based view and strategic human capital resource literatures. Journal of Management, 47(7), 1771-1786.
Prentice, C., Dominique Lopes, S., & Wang, X. (2020). The impact of artificial intelligence and employee service quality on customer satisfaction and loyalty. Journal of Hospitality Marketing & Management, 29(7), 739-756.
Radonjić, A., Duarte, H., & Pereira, N. (2022). Artificial intelligence and HRM: HR managers’ perspective on decisiveness and challenges. European Management Journal.
Rodgers, W., Murray, J. M., Stefanidis, A., Degbey, W. Y., & Tarba, S. Y. (2023). An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Human Resource Management Review, 33(1), 100925.
Sakka, F., El Maknouzi, M. E. H., & Sadok, H. (2022). Human resource management in the era of artificial intelligence: future HR work practices, anticipated skill set, financial and legal implications. Academy of Strategic Management Journal, 21, 1-14.
Schwartz, M. S. (2016). Ethical decision-making theory: An integrated approach. Journal of Business Ethics, 139, 755-776.
Shikalepo, E. E. (2020). Defining a conceptual framework in educational research. Namibia University of Science and Technology 7p.
Slimi, Z., & Carballido, B. V. (2023). Navigating the Ethical Challenges of Artificial Intelligence in Higher Education: An Analysis of Seven Global AI Ethics Policies. TEM Journal, 12(2).
Tuffaha, M. (2022). Adoption factors of artificial intelligence in human resource management (Doctoral dissertation, Universitat Politècnica de València).
Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022). Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review. The international journal of human resource management, 33(6), 1237-1266.
Winata, S., Kusnawan, A., Limajatini, L., & Hernawan, E. (2020). Ethical Decision Making Based On The Literature Review Of Ford & Richardson 1962-1993. Primanomics: Jurnal Ekonomi & Bisnis, 18(1), 1-8.
Yarger, L., Cobb Payton, F., & Neupane, B. (2020). Algorithmic equity in the hiring of underrepresented IT job candidates. Online information review, 44(2), 383-395.
Zaineldeen, S., Hongbo, L., Koffi, A. L., & Hassan, B. M. A. (2020). Technology acceptance model’concepts, contribution, limitation, and adoption in education. Universal Journal of Educational Research, 8(11), 5061-5071.
Zhou, Y., Wang, L., & Chen, W. (2023). The dark side of AI-enabled HRM on employees based on AI algorithmic features. Journal of Organizational Change Management, 36(7), 1222-1241.
